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    "long_description": "Science Exchange powers R&D outsourcing for the world’s top life sciences companies. Our marketplace gives scientists access to the innovation and research they need and our platform fully automates R&D outsourcing from source to pay so scientists can focus on what they love—science.",
    "one_liner": "Powering scientific outsourcing",
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    "industry": "Healthcare",
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      "Enterprise"
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        "full_name": "Elizabeth Iorns",
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    "name": "Benchling",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "Biotechnology is rewriting life as we know it, from the medicines we take, to the crops we grow, the materials we wear, and the household goods that we rely on every day. Biotech R&D is radically transforming our world, but to move at the new speed of science, scientists need better technology.\r\n\r\nBenchling’s mission is to unlock the power of biotechnology. The world’s biotech leaders and innovators use our R&D Cloud to power the development of breakthrough products and accelerate time to milestone and market.",
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    "industry": "B2B",
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        "answer": "In the early days, most software investors were not excited about biotechnology. But one investor who was curious about biotechnology was Y Combinator. Paul Graham wasn’t bothered by our originally narrow focus. I remember him saying that people who work with biology are important and so to be the most important software for them will surely turn out to be valuable."
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    "name": "Immunity Project",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "We're developing a free vaccine to cure HIV and AIDs.  Immunity Project is a non-profit initiative dedicated to developing an HIV vaccine. We hacked the HIV life cycle to develop a vaccine that gives you the same power as HIV controllers. With success in our clinical trials, our goal is to give our vaccine away to the world for free.",
    "one_liner": "The free HIV vaccine.",
    "team_size": 11,
    "industry": "Unspecified",
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    "long_description": "One Codex is a data platform for applied microbial genomics, enabling new and valuable applications in clinical diagnostics, food safety, and biosecurity. Acquired by Invitae (NVTA) in February 2021. Spun out as an independent company in September 2022.",
    "one_liner": "Data platform for metagenomics and the microbiome.",
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        "full_name": "Nick Greenfield",
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        "founder_bio": "Founded One Codex (YC S14), the leading microbial genomics data platform, acquired by Invitae (NVTA) in February 2021 and spun out via an employee buyout in late 2022. VP of Product & Tech (Biosecurity) at Ginkgo Bioworks 2022-2023 (DNA, also YC S14). Reformed management consultant turned software, data, and bio nerd. Skier, cheese-lover, Vermonter, based in VT and Cambridge MA.",
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    "long_description": "Notable Labs is building a personalized drug discovery platform to identify treatment options for relapsed and refractory cancer patients — starting with blood cancer — to address the long tail of cancer treatment. We look at a person’s actual cancer cells, test combinations of FDA approved drugs, and see which combinations kill the cancer cells and leave the healthy cells alive. We focus on combinations because cancer is often difficult to target with just one drug and starting with a single drug can lead to resistant clones or relapse. We're building a highly automated lab in Foster City running on our custom software and are currently testing relapsed/refractory cancer patients as well as samples from a variety of pharma/biotech partnerships.\r\n\r\nhttps://www.notablelabs.com/careers",
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        "full_name": "Matthew De Silva",
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        "title": "Senior Software Engineer - Full Stack",
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        "url": "https://www.ycombinator.com/companies/notable-labs/jobs/ti2HBnyyd-product-development-scientist-senior-assay-development-scientist",
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        "url": "https://www.ycombinator.com/companies/notable-labs/jobs/kF9VM8qjw-principal-product-development-scientist",
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    "long_description": "The next decade is going to see a biotech revolution fueled by three technologies: ability to read DNA (sequencing), ability to write DNA (synthesis), and computational systems that predict what DNA to read and write. With sequencing and synthesis being mainstream now [1], 20n provides the computational systems that predict DNA design for novel industrial biotech and health applications.\n\nAt 20n, we are taking a fresh look at turning biological data into information. We approach it as a big data learning problem, and bring to bear techniques from machine learning, distributed systems, AI planning, program synthesis, data mining, and natural language processing. With the support of DARPA [2], we are building a team of computer scientists, and bioengineers to look at biology in fundamentally new ways.\n\nOur current customers include industrial biotech firms that are leading the charge in transitioning us from traditional unsustainable means of manufacturing to sustainable biological processes leading to advanced fuels, materials, therapeutics, and flavors and fragrances. 20n’s technology helps them enter markets previously thought inaccessible for biology.\n\n[1] Sequencing DNA costs $4k/genome (http://www.genome.gov/sequencingcosts/) and is expected to go down to $1k/genome. Synthesizing DNA is at 20cents/bp, and is expected to go down to 0.1cents/bp.\n[2] DARPA in the past supported work that led to the internet, stealth planes, GPS, and even Douglas Engelbart’s mother-of-all-demos.",
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        "full_name": "Miroslav Gasparek",
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        "full_name": "Marian Kupculak",
        "title": "Founder",
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        "date": "Apr 04, 2023"
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        "url": "https://endpts.com/ginkgo-partners-up-with-sensible-biotechnologies-to-forge-mrna-manufacturing-platform/",
        "date": "Apr 03, 2023"
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        "url": "https://www.ginkgobioworks.com/2023/04/03/developing-a-novel-mrna-manufacturing-platform-with-sensible-biotechnologies/",
        "date": "Apr 03, 2023"
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        "date": "Apr 03, 2023"
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        "title": "Sensible Biotechnologies: Engineering cells to make better mRNA 🧬",
        "tagline": "mRNA. Made better.",
        "body": "At Sensible Bio, we are turning living cells into cost-efficient factories for manufacturing high-quality mRNA to unlock the full potential of mRNA therapeutics and vaccines.\n\n### **tl;dr**\n\nMessenger RNA (mRNA) is a promising biological platform for both vaccines and therapeutics. Today, all mRNA is synthesised via cell-free processes, which are expensive, cumbersome, and cannot produce mRNA with sufficient quality for many of its promising clinical applications. **We engineer cells to produce high-quality mRNA in a scalable and cost-efficient fashion to meet the growing demand for mRNA in novel vaccines and therapies.**\n\n### **The Problem: Cell-free mRNA manufacturing**\n\nCurrently, mRNA production completely relies on In Vitro Transcription (IVT), a cell-free process involving numerous proprietary and expensive enzymes and reagents. This method also requires multiple intricate steps, laborious purification procedures, and cannot be scaled to large reaction volumes.\n\nImportantly, IVT-based mRNA manufacturing generates highly immunogenic double-stranded RNA (dsRNA), which is posing risks in some of the most important applications of mRNA such as in gene editing and cancer. Moreover, it is difficult to produce and purify long mRNA sequences via IVT; this creates major obstacles for other promising mRNA modalities like self-amplifying mRNAs, which could significantly reduce the high amounts of mRNA needed for many of its therapeutic applications.\n\n**To truly unleash the power of mRNA more efficient, affordable, and effective mRNA manufacturing methods are needed.**\n\n![uploaded image](/media/?type=post&id=70713&key=user_uploads/89898/0eb9ee01-1c73-46bd-b15f-3bc3376e2176)\n\n## **The Solution: Cell-free mRNA manufacturing (not done)**\n\n**We are utilizing the processes in the living cells, which have been producing high-quality mRNA for millions of years.** By combining natural cellular processes with cell engineering, we build a scalable platform to produce high-quality mRNA cost-effectively.\n\nOur platform enables the production of **longer mRNA**, while addressing the problem of **immunogenic double-stranded RNA** inherent in IVT.\n\nBy removing the need for expensive and proprietary reagents, we enable **cost-effective mRNA manufacturing** and improve access to mRNA technology. Also, the **scalability** of cell-based mRNA production strengthens biosecurity and supply chain resilience.\n\nOur objective is to become a partner for all pharma and biotech companies striving to produce the next generation of mRNA therapies and vaccines.\n\n## **The Platform**\n\nOur platform combines the principles of **cell engineering**, **computational** **biology** and **precision** **fermentation**. We exploit and optimise the selective packaging of the mRNA of interest into specific protein complexes. This enables a specific encapsulation of mRNA, prevents mRNA contamination and simplifies its downstream processing.\n\n![uploaded image](/media/?type=post&id=70713&key=user_uploads/89898/01d827a8-d966-4616-a155-5a029401da1d)\n\n## **Team**\n\nSensible Bio was founded in August 2021 by **Miroslav Gasparek** (CEO) and **Dr Marian Kupculak** (CSO).\n\n**Miro** holds a Bioengineering degree from Imperial College London and conducted research at Imperial College, Caltech and Stanford. He worked with several TechBio VCs and advised public and private institutions in Central Europe on life science R&D. Miro also advised the Slovak Ministry of Health and the Board Member of the World Health Organization on biosecurity and innovation.\n\n**Marian** has a PhD in Molecular Biology from Oxford, where he focused on genetic engineering and cancer. He has more than 9 years of full-time wet-lab experience with cellular engineering. He has worked at the leading European research centres, including the University of Oxford, the University of Gothenburg, and Vienna BioCenter.\n\nOur leadership includes the **global leaders in mRNA manufacturing**, as well as in **clinical development and strategy**. We also assembled a fantastic team of bioengineers and scientists from across the globe.\n\n## **Traction**\n\nUsing our proprietary technology we have demonstrated the **capability to produce the mRNA**. Additionally, we have secured a major [co-development partnership with Ginkgo Bioworks](https://www.ginkgobioworks.com/2023/04/03/developing-a-novel-mrna-manufacturing-platform-with-sensible-biotechnologies/). We also set up collaborations with leading research institutions across the globe. Sensible Biotechnologies has established lab operations in **Oxford, United Kingdom** and **Bratislava, Slovakia**. We have obtained **10 Letters of Intent** for our mRNA from the biotech companies and research groups that would like to test it.\n\nOur **scientific advisory board** includes world-leading synthetic biology, mRNA manufacturing, and clinical experts and our mentors include the top life science executives.\n\nOur investors include **specialist** **TechBio** **funds** and leading **biotech executives** investing as angels.\\\n\\\nWe're excited to be working on unlocking the full potential of mRNA technology. Are you interested? Get in touch at [**info@sensible.bio**](mailto:info@sensible.bio).\n\nDo you want to build the platform for making better mRNA with us? Drop us an email at [**careers@sensible.bio**](mailto:careers@sensible.bio) We are hiring **synthetic biologists**, **bioprocess** **engineers**, **mRNA Quality Control experts**, **RNA computational biologists**, and **machine learning engineers.**",
        "url": "https://www.ycombinator.com/launches/IOX-sensible-biotechnologies-engineering-cells-to-make-better-mrna",
        "votes": 19,
        "created_at": "2023-04-03T16:24:05.337Z"
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    "name": "Output Labs",
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        "body": "**Have you experienced any problems with breast feeding? Or do you have any concerns with infant formulas? We would like to understand your concerns as we are creating real human milk using biotechnology. Please share your contact info [here](https://forms.gle/gy64yfLuJ1MtnfmD8) and we will connect to schedule a quick 20 mins call to understand your concerns.**\n\n### **UnReal Human Milk! 🍼 👶**\n\n> **_Why? - Lactation Issues in Women are at an all-time High!_**\n\nEvery year around 85 million women globally struggle with breast feeding in some way or the other. The existing infant formulas are largely based on cow milk which naturally lacks several essential components of human milk like lactoferrin, HMOs etc. which are critical for the nutrition of the baby. Recently there was one of the biggest recalls in history of infant formula in Dec 2025, with a similar recall in US in 2022 which led to sudden shortage of infant formulas and left parents extremely worried and vulnerable. To circumvent such incidents we need more clean-label innovation in this space to create better and more robust solutions for infant nutrition.\n\n> **_What and How? - Harnessing Biology Responsibly!_**\n\n![uploaded image](/media/?type=post&id=98944&key=user_uploads/215532/88fc5eaf-d775-43a4-86a0-104692179c41)\n\n🚀 🚀 **Huge** **Breakthrough** - **We have been able to secrete Real Human Milk in lab!!!** We have validated all proteins by independent third party testing and have also tested for fats and carbohydrates to create a basic version of real human milk. By using mammalian cell culture we have designed a system to produce human milk in lab with a macronutrient focus currently. We are a Stanford, Rice University and IIT Delhi alumni team with backgrounds across biotechnology, business and innovation, determined to make a huge impact by using technology and leveraging recent advances in understanding of biological systems. This breakthrough leverages our extensive work on cow milk ([Forbes](https://www.forbes.com/sites/daphneewingchow/2025/02/24/first-lab-grown-whole-cows-milk-to-debut-in-the-us/)) as we add this new product to our pipeline - UnReal Human Milk.\n\n> **_More Exciting Stuff! - Why should human adults drink cow’s milk and not human milk?_**\n\nUntil now we had no way to mass produce human milk so we resorted to drinking cow milk since we could keep the poor cows in captivity to produce as much milk as needed. But now with our standardised scalable approach to creating human milk maybe we can move everyone across the planet from drinking cow milk to human milk. Think human milk ice-creams and human milk cheeses - the opportunities are huge!\n\n![uploaded image](/media/?type=post&id=98944&key=user_uploads/215532/0b9233c8-9808-4043-83e6-e350806da38b)\n\n> **Reach-out to us** at [**sohail@brownfoods.in**](mailto:sohail@brownfoods.in) if you want to collaborate, invest or partner with us on this journey.\n\nThank you!\n\n![uploaded image](/media/?type=post&id=98944&key=user_uploads/215532/db9850e5-7d88-42fb-9ea4-871d78b1dd1f)\n\n",
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        "title": "🌳 Birch Biosciences — Engineered Enzymes for Plastic Recycling 🌳",
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        "body": "**The problem:**\n\nToday, plastic recycling doesn't work.  Less than 9% of plastic is recycled in the United States, a number unchanged over the past 15 years.  The current mechanical recycling process involves melting and reforming plastic at high temperatures, generally resulting in major greenhouse gas emissions while producing low quality plastic at high cost.  This is a major problem as consumers demand more sustainable plastic products, but brands are unable to meet this demand due to sustainability, quality, yield, and price issues with mechanically recycled plastic.  If we don’t develop a circular plastic economy, plastics will be responsible for 15% of global CO2 emissions by 2050.\n\n**The solution:**\n\nBirch Biosciences is engineering an enzyme-mediated process to recycle plastic. Our high performance enzymes work as “molecular scissors'' at low temperatures to break down plastic polymers into monomer “building blocks,” which can be re-polymerized into high quality recycled plastic products. Birch recycling technology will:\n\n* **Reduce** **carbon emissions** associated with plastic recycling by > 70%.\n* Generate recycled plastic of **equivalent** **quality** **to new plastic**.\n* Produce recycled plastic at **equal or lower cost** than mechanically recycled plastic.\n* Increase the variety of plastic products that can be **effectively recycled**\n\nBirch’s recycling process enables a true ‘circular’ plastic economy where plastic products are continuously recycled into the same plastic products, rather than being manufactured from oil or gas.  This is especially important as consumers, brands, and our planet demand more sustainable products.\n\n**Our technology focus:**\n\nEnzymes as “molecular scissors” are the centerpiece of our recycling technology. We discover and develop high performance enzymes using iterative machine learning and quantitative high-throughput screening of novel protein designs. Our technology stack enables design of robust enzymes for industrial scale plastic recycling using a fermentation-like bioprocess.\n\nWe’re currently focused on engineering enzymes for PET plastics (i.e., beverage bottles, polyester clothing) and polyurethane plastics (i.e., foam for furniture, bedding, insulation). In addition to YC, our technology development is supported by the US Department of Energy and the National Science Foundation.\n\n**How you can help:**\n\nIf you or anyone in your network might be interested in learning more or helping out, we would love to chat.  We're especially interested in talking with:\n\n* Companies and brands that use PET plastics\n* Experts in the plastic manufacturing and recycling industry\n* Operators of recycling facilities\n* Nonprofits with an interest in sustainability and plastic recycling\n\nWe can be reached at [founders@birchbiosciences.com](mailto:founders@birchbiosciences.com)",
        "url": "https://www.ycombinator.com/launches/Gix-birch-biosciences-engineered-enzymes-for-plastic-recycling",
        "votes": 246,
        "created_at": "2022-07-02T17:42:00.694Z"
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    "id": 26734,
    "name": "Serinus Biosciences",
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    "all_locations": "New York City, NY, USA; New York, NY, USA",
    "long_description": "Serinus Biosciences is tackling one of the toughest problems in cancer treatment: creating combination therapies to override treatment resistance. We leverage cutting edge technology to design a fully explainable AI platform primed with decades of system biology knowledge. Uniquely powered for biological inference, our AI engine uncovers how cancer cells evolve treatment resistance and identifies molecules to overcome resistance escape routes. We design combinations that are safer, more effective, and can get to patients quickly. Founded by MIT PhDs and supported by a scientific advisory board of top academics from the Broad Institute, Dana-Farber, and UCSD, Serinus is powered by Y Combinator and other top investors to revolutionize precision medicine.",
    "one_liner": "Combinations therapy by rational design",
    "team_size": 6,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Therapeutics",
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        "full_name": "Adam Yaari",
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        "founder_bio": "Prior to founding Serinus, Adam gained over 10 years of entrepreneurship experience including a Microsoft R&D Fellowship and Flagship Pioneering Ideation Fellowship. Adam is passionate about developing advanced AI models for biological research that will bring medicine to scale.\nAdam received his PhD and MS in computer science from MIT, MSc in computer science and mathematics from the Weizmann Institute of Science, and dual major BSc in computer science and neuroscience from Bar-Ilan University.",
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        "full_name": "Maxwell Sherman",
        "title": "Founder/CTO",
        "founder_bio": "After five years as a paramedic, Max left clinical medicine to develop new treatments for the millions of patients without access to effective medications. He studied applied math and biology at Brown University, statistics at Cambridge University, and is completing his PhD in computational biology at MIT. His research has appeared in prestigious journals including Science, Nature Biotechnology, and Nature Neuroscience and has been recognized with national and international awards.",
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        "title": "Serinus Biosciences: Killing cancer with its own genetics 💪🏽",
        "tagline": "We develop drugs effective against multiple types of cancer... and we do it really fast! 🏃🏽",
        "body": "We're **Adam** and **Max**, the founders of **Serinus Biosciences** 💊💻🤓\n\nAt Serinus, we develop drugs that are effective against **multiple types of cancer**, and because cancer patients need solutions now, we do it in **months instead of years!**\n\n### **_Cancer: an insidious disease with insidious economics_** 💰💰💰\n\nCurrently, **more than 80% of cancer patients** are not eligible for targeted cancer therapies - the most efficient and least toxic cancer treatments. _Why?_ Because the proteins driving cancer in one patient are often different than those driving cancer in another patient.\n\nHere's where the economics comes in. The cost of bringing a new cancer drug to market is currently **>$2.5 BILLION**. Developing a drug against a protein that only causes cancer in a small number of patients each year is just not economically viable with traditional drug development.\n\n### **_The Serinus Strategy_** 💡\n\nWhile building AI tools to understand the causes of cancer during our PhDs at MIT, we had a realization: all the different cancer-causing proteins disrupt the same cellular systems. **We shut down these shared dysfunctional systems.**\n\n_How?_ We look for the support proteins that keep the system functioning. Think about a telephone system ☎️ If you want to bring it down, you should find the small number of relay stations that route messages across the network instead of hitting the individual phones. **We find the relay stations of cancer cells and bring them dow**n ☠️☠️☠️\n\nSince cancer systems are widely shared across patients, the relays that support those systems are widely shared across patients. Our current pipeline **could help more than 100K PATIENTS PER-YEAR!**\n\n### **_The knowledge (and stubbornness) to get it done_** 🤓 😎\n\nWe met on the very first day of our PhDs at MIT. It was a cancer-fighting match made in heaven. Adam comes from an AI/ML background, Max comes from a biology and statistics background, having given up his place in medical school so he could develop new treatments for patients in need by merging biology, mathematics, and computer science.\n\nOh, and did we mention that Adam is a former Navy SEALs commander and Max spent five years as a paramedic.\n\n**_How can you help?_**\n\nOur platform is churning out way more ways to kill cancer than we can pursue ourselves. If you or someone you know works at another therapeutics company that might be interested in co-developing a drug with us, give us a shout on [founders@serinus.bio](mailto:founders@serinus.bio)",
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        "votes": 39,
        "created_at": "2022-08-23T01:28:16.522Z"
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    "id": 26751,
    "name": "Signatur Biosciences",
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    "all_locations": "London, England, United Kingdom",
    "long_description": "We make precision diagnostics more accessible, starting with breast cancer prognosis. Current tests that guide the use of therapy are really slow and expensive because they need to be run in complex centralized facilities. Our tests are designed to be easily run by local hospital labs, reducing turnaround time from over three weeks to a few days and for a fraction of the price.",
    "one_liner": "Simple tests for complex diseases.",
    "team_size": 5,
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    "year_founded": 2022,
    "city": "London",
    "country": "GB",
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        "full_name": "John Goertz",
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        "founder_bio": "Co-founder, CSO, and PCRchitect at Signatur Biosciences. I teach machine learning to molecules to expand the range of diseases we can diagnose with PCR. NIH F32 Postdoctoral Fellow in the the Stevens Group at Imperial College London; PhD Bioengineering from University of Maryland, College Park; BSc Physics and Cell & Molecular Biology from Seattle University. When I’m not programming DNA, I’m cooking, running, or doing martial arts (Danzan-Ryu Japanese Jujitsu).",
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        "user_id": 924455,
        "full_name": "Celestin de Wergifosse",
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        "founder_bio": "Co-founder and CEO of Signatur Biosciences. Startups since the age of 14. First patent at 15. WIPO Best Young Inventor 2010. Twice TEDx speaker. Co-founded Young Change Maker, a social startup that highlights inspiring individuals. Co-founded and sold a company called Sauna en Bois. Bio-engineering from Imperial College London. When I don't work, I race motorcycles, climb or ski. My life-long goal is to develop biotech companies that address global issues.",
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        "title": "Lab Officer (Liège, Belgium)",
        "url": "https://www.ycombinator.com/companies/signatur-biosciences/jobs/RCUUWfh-lab-officer-liege-belgium",
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        "title": "A simple PCR to predict the effectiveness of chemotherapy (translated)",
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        "id": 64421,
        "title": "🧪 Signatur Biosciences diagnoses anything with a simple PCR test.",
        "tagline": "We expand PCR testing to a whole new range of diseases!",
        "body": "**tl;dr:** Signatur Biosciences is a B2B molecular diagnostics design company. Our technology expands PCR testing to a whole new range of conditions. We design smart PCR kits that can detect complex diseases as easily as a COVID test, using standard qPCR machines. Our tests seamlessly integrate into existing clinical workflows and ubiquitous laboratory instruments, empowering us to become _the_ enabling technology platform for a $20B industry.\n\n👋 Hey everyone! We’re Celestin and John. We are determined to transform the state of medical diagnostics. We set out to find a solution that delivers state-of-the art diagnosis on ubiquitous instrumentation to put cutting-edge healthcare within the reach of everyone, everywhere.\n\n### **The Problem.**\n\nRight now, PCR testing is limited to simple diseases. Covid is a good example. You take a sample from a patient and look for one biomarker. If the biomarker is there, the patient has the disease; no biomarker, no disease. Simple.\n\nBut most conditions aren't that simple. There’s a $20B industry using _patterns_ of multiple biomarkers that diagnose complex diseases, but to deploy their tests they’re relying on legacy technologies that are too slow and expensive to really help people, limiting their market reach.\n\n### **🧬 Signatur Biosciences’s solution.**\n\nOur platform expands PCR testing to a whole new range of conditions, beyond just infectious disease to include everything from chronic diseases, treatment selection, and disease monitoring.\n\nWe have re-engineered the molecular architecture of the polymerase chain reaction, empowering it to process the information from multiple biomarkers simultaneously. We combine the power of gene signature diagnosis with the **_ease and familiarity of a single PCR reaction._**\n\nEvery biomedical lab in the planet is equipped with PCR machines. This was true before the pandemic. But as a response to the pandemic, the PCR testing capacity in the US has increased by 5500% (3000% in the UK). We are giving those machines something new to do.\n\n### **🚀 How can Signatur Biosciences help me?**\n\nWe are partnering with companies to design our platform around their biomarker panels, offering broader market reach bringing such tests to more patients.\n\nIn parallel, we are building our own assay portfolio. We are developing our own internal pipeline of tests to tackle diseases that can only be interrogated by our technology.\n\n### **How you can help.**\n\nWe are looking for introductions to companies in the molecular diagnostics space. This could be companies developing portable hardware. Specifically developing genomic testing, or assays based on gene expression analysis.",
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    "id": 26769,
    "name": "ALT TEX",
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    "website": "https://thealttex.com/",
    "all_locations": "Toronto, ON, Canada",
    "long_description": "ALT TEX is a Toronto-based biomaterials startup that has developed the world’s first biodegradable and carbon neutral fabric, engineered from the world’s largest landfill contributor - food waste. Polyester alone makes up 60% of the $2.5 trillion fashion industry and is the biggest blocker preventing brands from meeting sustainability mandates. The patent-pending ALT TEX fermentation technology creates a polyester alternative that works with the existing infrastructure to supply a sustainable, high performance and cost competitive polyester replacement to fashion brands, at scale. \r\n\r\nThe product solves key problems for fashion brands, consumers, and the environment:\r\n\r\n(1) Fashion brands: ALT TEX sells fabric to fashion brands, allowing them to differentiate via sustainable options (sustainable fashion growing 11% CAGR) and hit sustainability targets, without sacrificing performance or changing their current supply chain.\r\n\r\n(2) Consumer: The ALT TEX fabric is projected to be half the price vs. other specialized sustainable alternatives (due to low-cost input and scalability), meaning greater accessibility.\r\n\r\n(3) Environment: A single shirt produced with ALT TEX fabric can divert 1 kg of food waste from landfills, 9 kg of carbon emissions from the air, and 4 g of micro-plastics from our waterways.\r\n\r\nCo-founders Myra and Avneet have worked together managing teams and projects for six years, with backgrounds in serial entrepreneurship, corporate sales at Fortune 500 companies, textile manufacturing and biochemistry. To date, ALT TEX has created their first fabric prototype that’s 70% stronger than cotton, secured $1M in LOIs and raised $2.3M.",
    "one_liner": "Biodegradable and carbon neutral textiles, engineered from food waste.",
    "team_size": 10,
    "industry": "Industrials",
    "subindustry": "Industrials -> Climate",
    "launched_at": 1659631637,
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    "year_founded": 2020,
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        "full_name": "Myra Arshad",
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        "founder_bio": "Hey, I'm Myra - Co-founder and CEO of ALT TEX.  Coming from a family background in textile manufacturing, I'm disrupting the $2.5 trillion fashion industry with better and more sustainable materials.\n\nPrior to ALT TEX, I held corporate roles in sales and marketing at Fortune 500 companies, before founding fluidic – a direct to consumer lifestyle brand. Prior to fluidic, I built FarmBox, a Hult Prize backed, AI micro-farming technology that enabled food sustainability in refugee camps.",
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        "full_name": "Avneet Ghotra",
        "title": "Founder",
        "founder_bio": "Hi, I'm Avneet - Co-founder and CTO of ALT TEX. I left medical school to build ALT TEX. With education and research background in biochemistry & environmental science, I lead the R&D behind ALT TEX’s food waste to fabric mission. Prior to medical school & ALT TEX, I founded Approaching Zero, a non-profit dedicated to reducing plastic pollution through production of reusable cotton produce bags.  Continuing that vision, I am now building a global solution for fashion's polyester pollution. ",
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        "date": "Aug 04, 2022"
      },
      {
        "title": "How Canadian clothing entrepreneurs are disrupting 'fast fashion' | CBC News",
        "url": "https://www.cbc.ca/news/climate/canadian-companies-environmental-fashion-solutions-1.6429603",
        "date": "Aug 04, 2022"
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        "url": "https://betakit.com/alt-tex-closes-1-5-million-pre-seed-round-to-commercialize-sustainable-polyester-alternative/",
        "date": "May 19, 2021"
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        "title": "ALT TEX: Fabric made from food waste 🍎👕",
        "tagline": "Biodegradable, carbon neutral, cost competitive and high performance fabrics, engineered from the world’s largest landfill contributor - food waste.",
        "body": "Hey All! We’re [Myra](https://www.linkedin.com/in/myra4/) and [Avneet](https://www.linkedin.com/in/avneet-ghotra-51284728/), co-founders of [ALT TEX](http://thealttex.com). We’ve created the world’s first polyester-alternative fabric, made from food waste.\n\n![uploaded image](/media/?type=post&id=65136&key=user_uploads/578199/71da3b57-6b33-4db2-a80f-becf0dd70065)\n\n🌍 **The problem: polyester is leading fashion’s GHG emissions, but has no circular alternative.**\n\nThe fashion industry’s heavy reliance on petroleum-derived polyester textiles is a major contributor to the 10% of global GHG emissions the industry generates annually. The plastic-based material, found in 60% of the clothes we wear, creates over 50 million tons of global annual landfill waste, generates 35% of all ocean micro-plastics, and consumes 70 million barrels of oil each year.\n\nDespite 1/3 of the fashion industry committing to 0 carbon emissions by 2050, polyester, the most widely used fabric, has no fully circular, carbon-neutral alternative.\n\nAt the same time, food waste is on the rise with 1 billion tons generated annually across the world. So, we’re using one problem 🍎 to fix the other 👕. Here’s how ⬇️\n\n👕 **The Solution: Fabric created from the world’s largest waste source - food waste.**\n\nALT TEX reduces the fashion industry’s dependence on fossil fuels through its biodegradable and carbon-neutral textiles engineered from food waste. The patent-pending fermentation technology plugs into the existing polyester supply chain to provide fashion brands with a cradle-to-cradle polyester alternative without sacrificing performance.\n\nThe product solves key problems for fashion brands, consumers, and the environment:\n\n1. Fashion brands: ALT TEX sells fabric to fashion brands, allowing them to differentiate via sustainable options (sustainable fashion growing 11% CAGR) and hit sustainability targets, without sacrificing performance or changing their current supply chain.\n2. Consumer: The ALT TEX fabric is projected to be half the price vs. other specialized sustainable alternatives (due to low-cost input and scalability), meaning greater accessibility.\n3. Environment: A single shirt produced with ALT TEX fabric can divert 1 kg of food waste from landfills, 9 kg of carbon emissions from the air, and 4 g of micro-plastics from our waterways.\n\n**The Story & Future:**\n\nMyra and Avneet have worked on every school project together since the 9th grade. With Myra’s background in corporate sales and family history in the textile sector, alongside Avneet’s background in environmental science and biochemistry, the two came together in 2020 to build ALT TEX. Today, ALT TEX has created its fabric prototype, raised $2,300,000 USD in funding, secured $1,000,000 in pilot LOIs and has grown the team to 10.\n\n**How you can help:**\n\nAs we scale out of the lab and into the market over the next year, we’re looking for:\n\n1. fashion brands interested in sourcing a sustainable polyester alternative\n2. food manufacturers looking for a sustainable way to re-purpose their waste\n\nIf you’re curious or have help/resources for us, please feel free to reach out!\n\n📩 [myra@thealttex.com](mailto:myra@thealttex.com)\n\n🌐 [thealttex.com](http://thealttex.com) | [linkedin.com/company/alt-tex/](http://linkedin.com/company/alt-tex/)\n\n👩‍💻 [@thealttex](https://twitter.com/thealttex)",
        "url": "https://www.ycombinator.com/launches/Gwa-alt-tex-fabric-made-from-food-waste",
        "votes": 188,
        "created_at": "2022-08-04T17:08:53.742Z"
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        "id": 69547,
        "title": "⚛️ Lavo Life Sciences - Computer simulations of drug molecules",
        "tagline": "We use AI to de-risk drug development and save time in the lab",
        "body": "### **TL;DR**\n\nLavo Life Sciences runs simulations of drug molecules on computers. Pharma companies use our simulations to guide their experiments, saving hundreds of millions of dollars and years of time in the lab. This will de-risk clinical trials and expedite FDA approval.\n\n![uploaded image](/media/?type=post&id=69547&key=user_uploads/148651/80bc9d1a-e8b4-41cc-8ca1-5deed312ecbc)\n\nThis is what a pill looks like at the molecular level. Drug molecules pack into repeating patterns–or “crystal structures”–which control the drug’s stability. This particular drug (Ritonavir) was recalled when the developers chose an unstable crystal structure. This recall could have been avoided with effective simulation.\n\n### **🤔 The Problem**\n\nLavo’s simulation software predicts the crystal structure of drug molecules. The crystal structure refers to the three-dimensional pattern in which individual molecules pack to form a solid. Choosing the best crystal structure before clinical trials maximizes the efficacy, shelf-stability, and safety of the drug. Failure to accurately determine the crystal structure can lead to problems in manufacturing, legal protection of the drug patent, and success in clinical trials.\n\nFrom conversations with pharma companies, we’ve learned that they want more of these predictions, but existing vendors are too slow and expensive–think hundreds of thousands of computer hours for a single molecule. This is a limitation of their methodology and is also our key differentiator.\n\nThis market is large and growing. 15,000 relevant drugs enter R&D each year, and pharma companies spend over $100M on crystal structure predictions annually. Also, we have learned that pharma companies need predictions on larger drugs than are currently possible with today's methods, so we have an opportunity to expand the market with more efficient products.\n\n### **🔨 Why now, why us?**\n\nWe are uniquely positioned to solve this problem – Zach and Derek have PhDs in computational chemistry and Scott is a former Google Cloud PM. We are prepared to tackle this scientific challenge at an industrial scale.\n\n### **💡 Our Solution**\n\nWe started working on this problem 4 months ago and our simulations are already 10x faster than current methods\n\nWe use AI and quantum chemistry algorithms to improve every step of the crystal structure prediction workflow:\n\n* We use custom algorithms to quickly get an understanding of the landscape of possible crystals.\n* We use AI to accurately rank crystal structures 100x faster than traditional techniques\n* We scale our software on the cloud to achieve rapid turnaround times.\n\n### **❤️ Our Asks**\n\nWe are interested in meeting people in the pharma industry. Please email [founders@lavo.ai](mailto:founders@lavo.ai) to connect with us!\n\nThanks,\n\nScott, Zach, and Derek",
        "url": "https://www.ycombinator.com/launches/I5j-lavo-life-sciences-computer-simulations-of-drug-molecules",
        "votes": 30,
        "created_at": "2023-02-22T17:14:00.733Z"
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    "id": 27263,
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    "long_description": "Your immune system is your body’s best weapon and defense against cancer. It is only when it is overcome that cancer develops into full blown disease. Dendritic cells sit at the helm of the immune system, directing and dictating immune responses. Data has shown dendritic cells are often dysfunctional in cancer patients, and that this dysfunction is a bottleneck for other therapies. \r\n\r\nGuardian Bio’s approach is focused on creating a dendritic cell-based product which drives anti-tumor activity through activating the killers of the immune system AND training them against multiple cancer targets. We take stem cells from cancer patients, turn them into specific therapeutic dendritic cells specially trained against that patient’s tumor, and re-introduce them back into the body. \r\n\r\nOur therapeutic dendritic cells revitalize the killers of the immune system and give them the kickstart they need to fight - and win - against cancer.",
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        "full_name": "Ananya Zutshi",
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        "founder_bio": "Co-founder and CSO at Guardian Bio.\n\nRicky started his career as a marine biologist and research diver at FAU with a focus on marine natural products and drug discovery.  \n\nHe pursued a PhD at UCF, focused on immunology followed by a postdoc in cancer immunology and tumor microenvironment at the University of Pennsylvania.\n\nRicky is a prolific science communicator creating educational science content for an audience of over a quarter million followers across various social media platforms.",
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        "title": "Guardian Bio - Control Immunity with Dendritic Cells",
        "tagline": "We make dendritic cells from a patients own stem cells and program them to cure diseases, starting with cancer.",
        "body": "# tl;dr\n\nYour immune system keeps you healthy. If it loses control, diseases like cancer, chronic infections, and autoimmunity can develop. We created a platform that enables us to produce a master regulator of the immune system (the dendritic cell) from patient stem cells. We program these cells so that they can make corrections to dysfunctional immune systems and bring disease back under control. We’re starting with cancer.\n\n# Team\n\nPrior to starting Guardian Bio, Ananya…\n\n* Was a Blavatnik Fellow at Harvard Business School, a fellowship fostering life science entrepreneurship \n* Worked across Operations, Engineering, and Project Management in the manufacturing and development side of the pharma/biotech industry\n* Received dual Masters in Engineering Sciences and MBA from Havard, where she was a Kaplan Fellow, and graduated with honors from Duke with a degree in Biomedical Engineering\n\nPrior to starting Guardian Bio, Ricky…\n\n* Was a Postdoctoral Fellow at University of Pennsylvania - tumor microenvironment, tumor immunity, and cell therapy\n* Co-Founded the Philadelphia chapter of Nucleate, a free and collaborative trainee-led organization that facilitates the formation of pioneering life sciences companies\n* Earned a PhD in Biomedical Sciences - Immunology at University of Central Florida\n* Worked as a marine biologist and scientific diver at Florida Atlantic University\n* And continues to create science communication content for social media platforms, reaching an audience of over a quarter million followers across TikTok and Instagram\n\n# The Problem\n\nThe overall problem is that the immune system unfortunately can’t always maintain control over disease. Cancer specifically actively suppresses the immune system. It has to. If the immune system maintains control, it will kill the cancer and disease will not develop. \n\nOne of the immune cell types often suppressed by cancer are dendritic cells, which sit at the helm of the immune system. Dendritic cells (DCs) direct and dictate immune responses. DCs are often deficient or dysfunctional in cancer patients, creating severe limitations in the body’s ability to fight off cancer, even when treatments like chemo and immunotherapy are applied.\n\n# The Solution\n\nWe take stem cells from cancer patients, turn them into dendritic cells, program them against that patient’s tumor, and re-introduce them back into the body. This stimulates a diverse yet targeted and robust immune response against the cancer. \n\n# The Platform\n\nOur dendritic cell-based platform can be adapted to a variety of diseases. When we train DCs against different targets, our therapy can reignite immune responses against chronic infectious diseases or suppress immune cells triggering autoimmunity.\n\nOur dendritic cell-based therapy is synergistic with many other immunotherapies including checkpoint inhibitors, peptide and mRNA-based cancer vaccines, oncolytic viruses, etc, potentially extending the reach of these therapies to patients who would not have responded before.\n\n![](https://lh3.googleusercontent.com/Fog7y2f3-PsbDww_4qw_LbLzzzM_3yXUEMVPMps50vCfN5dnHauQFTn72dLW2hd1RjKghsjpl8HwpSrJ_zMOe-QneWuKm_BLkumbU-01HqsRlLddaJ8713teujTfrYSRd0y9lsxxqffoBgvUwzGsXg)\n\n",
        "url": "https://www.ycombinator.com/launches/H4J-guardian-bio-control-immunity-with-dendritic-cells",
        "votes": 21,
        "created_at": "2022-08-25T13:47:55.461Z"
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    "long_description": "Miracle is building the control room for clinical trials. Today, biotech companies rely on fragmented software systems, spreadsheet trackers, and manual processes to manage complex clinical trials. Miracle integrates and consolidates data from disparate sources into a single platform, giving teams real-time visibility, helping them identify risks early, and streamlining workflows to make faster, better decisions.\r\n\r\nBy unifying clinical trial data, we're also laying the foundation for AI-powered analytics and automation.\r\n\r\nOur mission is to modernize how clinical trials are run so new treatments can reach patients faster.",
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        "body": "Hi 👋🏾 I’m Namrata, founder of ⚛️⚛️⚛️ **Diffuse Bio!** ⚛️⚛️⚛️\n\nAt Diffuse, we computationally design therapeutics, vaccines, and enzymes better, faster, and cheaper — but most importantly, we generate **molecules that simply can’t be designed with existing methods today**.\n\n### The Problem:\n\nProteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life. An important task in bioengineering is designing proteins with specific 3D structures and chemical properties which enable targeted functions. \n\nSo far, computational protein design methods have had some success — but have enormous limitations, the primary one being **low success in downstream validation experiments**. These methods are also painfully slow and scale poorly.\n\n### What we do:\n\nFor the past several years, I’ve been working on **re-envisioning the computational protein design toolkit with AI** in my PhD and beyond, with the goal of overcoming these challenges. Some highlights are the first crystal structures of [AI-designed proteins](https://www.nature.com/articles/s41467-022-28313-9) (i.e. experimental validation) and the **first diffusion models** for [protein structure and sequence generation](https://nanand2.github.io/proteins). Below you can see one of our early models producing structures unconditionally from noise!\n\n![uploaded image](/media/?type=post&id=69997&key=user_uploads/459396/08cd90de-6450-4ebe-8873-a77d625e0ed1)\n\nNow, we’re **scaling up these methods** and **applying them to grand challenge problems in molecular design**. We’re able to handle a whole array of protein engineering tasks at the _push of a button_ — from loop redesign and sequence engineering, all the way to binder design and _de novo_ structure generation. \n\n### Curious to learn more?\n\nCheck out our coverage in the [**New York Times**](https://www.nytimes.com/2023/01/09/science/artificial-intelligence-proteins.html#:\\~:text=Namrata%20Anand%2C%20a%20former%20Stanford%20University%20researcher.%20She%20is%20now%20building%20a%20company%20in%20generative%20A.I.%20protein%20design) and [**NBC news**](http://bit.ly/3JjgVKL), and get in touch at [info@diffuse.bio](mailto:info@diffuse.bio)!\n\n### We’d love your help!\n\n* If you’re working on any protein or molecular engineering problem, we’d love to hear from you!\n* We’d love **intros to pharma and biotech companies** working on problems in protein therapeutics, antigen design, and enzyme design. \n* We’d also love intros to any companies (preferably early stage) doing **high-throughput protein characterization** **assays** (yeast display, phage display, etc).\n* Sign up [here](https://docs.google.com/forms/d/e/1FAIpQLSdamv0S3LB2cyVLJXT6QdCG2g71Lobwkw59qTYGullYgqg7bw/viewform?usp=sf_link) to **beta test** our first-gen protein design software!",
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        "body": "### **Tl;dr**\n\nAt Invitris we're creating **protein-based drugs**, such as the next generation of antibiotics. To do this at scale, we created a platform called **Phactory**, that can turn DNA into synthetic proteins. Phactory makes the development of novel protein-based drugs 10,000x more efficient and cuts down the material costs by 100x.\\\n—\n\nHi everyone! This is [**Kilian** ](https://www.linkedin.com/in/kilian-vogele-21a321183/)and [**Patrick**](https://www.linkedin.com/in/patrickgrossmannbioinformatics/) from INVITRIS, a Munich-based life science company.\n\n![uploaded image](/media/?type=post&id=70166&key=user_uploads/89898/df875741-b00f-4ba8-b56f-48cee390fbf1)\n\n# **Protein-based drugs are powerful but expensive**\n\nWhile only 2% of all treatments are based on protein-based drugs, these drugs generate 40% of the total drug revenue. One of the reasons why they are so expensive is that you need living cells, like bacteria, to produce them. **Handling living cells is error-prone** and **scales terribly** because you have to establish new processes for every new drug.\n\n# **One platform to synthesize them all**\n\nOur universal biotech platform, **Phactory**, takes DNA and uses it to synthesize proteins in a single step. It mimics the molecular machinery you find in living cells but can be stored in conventional vessels.\n\n![uploaded image](/media/?type=post&id=70166&key=user_uploads/1163538/e720da18-e212-42d5-9a13-a5e53fbecc80)\n\nThis platform is based on Kilian’s groundbreaking research during his PhD, which was honored in the famous magazine [**Science** ](https://www.science.org/content/blog-post/bacteriophages-modern-way)and was also featured on [**German national TV**](https://www.zdf.de/nachrichten/zdf-morgenmagazin/moma-future-viren-gegen-bakterien-100.html)!\n\nOur primary focus area is **bacteriophages** - natural viruses that cannot attack humans but bacteria, even when those have become resistant to antibiotics. We are therefore tackling a problem that is expected to cause 25% more deaths per year than all cancers combined in the next decades: [multidrug-resistant infections](https://www.stopamr.eu/blog/new-studies/no-more-wake-up-calls-amr-is-now-the-leading-cause-of-death-worldwide/).\n\n# **We believe in collaboration and partnering!**\n\nBuilding new protein-based products is hard. We can help each other.\n\n* We are constantly looking for **biopharmaceutical partners** seeking innovation!\n* Intros to companies developing antibacterial products in animal health or agriculture are also welcome!\n* If you **struggle with protein synthesis** in general - we can help!\n* Know anyone who is experienced in scaling cell-free expression under GMP? Reach out!\n\nAny outreach is highly appreciated:<https://invitris.com/contact/>\n\n![uploaded image](/media/?type=post&id=70166&key=user_uploads/1163538/d016e457-3a55-4651-a7f6-6308d85772cb)\n\n",
        "url": "https://www.ycombinator.com/launches/IFi-invitris-simplifying-drug-development-manufacturing",
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        "title": "Modulari-T 🧬 Unleash the full power of the immune system",
        "tagline": "We teach your immune system to attack and cure cancer.",
        "body": "# Modulari-T AWAY!\n\nHi YC, this is [@David](https://www.linkedin.com/in/david-white-modulari-t/) and [@Stephanie](https://www.linkedin.com/in/stephmok/) from [@Modulari-T](https://www.linkedin.com/company/modulari-t-bio/) launching!\n\n### **WHAT WE DO**\n\nModulari-T is a cell therapy company that can teach your immune system to attack and cure cancer.\n\n \n\nWe are a dynamic group of scientists bringing together protein engineering, synthetic biology, and immunology to create the world’s most powerful cell programming platform.\n\n \n\nBy introducing our optimized gene sequences inside immune cells, we can create treatments that are 2x potent as chemotherapy, but that avoids 90% of harmful side-effects.\n\n \n\nToday, cell therapy has a high failure rate and is difficult to produce, leaving 20M patients without hope. Modulari-T has the technology to fix this, bring hope to cancer patients, and supercharge the cell therapy market to grow beyond $50B by the end of the decade.\n\n \n\n### **HOW YOU CAN HELP**\n\nConnect us with people in the ecosystem! Do you know anyone working in cell therapy or oncology? We want to talk to them, to know about their pain points - academic, biotech/pharma or physicians.\n\n[We are hiring!](https://modulari-t.breezy.hr/) Are you a synthetic biologist or immunologist? Ready to join a new adventure? Become part of the Modulari-T squad (Mod Squad!) in beautiful Montreal!\n\n### **THE SCIENCE**:\n\nWe have built a platform to produce a new type of receptor, the Modular Actuation Receptor Complex or MARC, that mimics the modular architecture of immune receptors. These receptors can be used to reprogram immune cells to recognize cancer antigens and direct an immune response against tumor cells. Compared to CAR-T cells, MARCs have twice the tumor clearing power and avoid causing immune exhaustion to ensure a long lasting response.\n\n![](https://lh6.googleusercontent.com/ItKO7egaIuynMGKcNjoT2YMqkANZ6E6oNxe5RyIisJ47KOaWhchZU9NpFHXfqkGu_EIB6--huJ4KOnT_5IqHMQut2czid5T8krpDXcZ3iSYTSEL9y2tyAg2IfOYSLic_Uaiz7dOJbcH6dELlc-MPEA)\n\nThe MARC’s unique architecture allows us to link any extracellular cue with the required signalling outputs across different pathways to achieve therapeutic success.\n\n![uploaded image](/media/?type=post&id=68886&key=user_uploads/1161771/abbed6b1-a7be-43e4-821c-8daa69fff47a)\n\nOncology is only the first step; the MARC platform is applicable to autoimmunity, neurology, regenerative medicine and more!\n\n### **HOW WE CAN HELP YOU**\n\nAre you a Cell Therapy company? Let us show you how Modulari-T’s MARC platform can help your development. Our platform can do anything but we can’t do everything alone. That’s why we want to partner with you and bring hope to as many patients as we can.\n\n### Thank you!\n\n![uploaded image](/media/?type=post&id=68886&key=user_uploads/1161771/773beb5d-3dbb-4905-8b5e-c5e453a33517)\n\n",
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        "body": "Pando is an AI-driven synthetic biology company revolutionizing enzyme engineering for pharma. Our ultra-high-throughput screening platform screens 1000 times more enzymes, 75% faster, and 80% cheaper than traditional methods. This empowers our generative AI to optimize enzymes across multiple properties, delivering high-performing, tailored enzymes that cut costs and enhance efficiency.\n\n### The Problem\n\nEnzymes play a crucial role in driving market growth and profitability in the pharmaceutical industries. Inefficient or underutilized enzymes can lead to significant financial losses and missed opportunities. Therefore, developing optimized enzymes that possess a balance of key properties becomes a top priority but remains challenging and time-consuming.\n\n### Our Solution\n\nOur AI model designs enzymes with specific mutations that enhance multiple properties simultaneously. These AI-generated enzymes are tested using our unique ultra-high-throughput one-pot screening platform. This platform converts diverse biological signals into abundant and quantifiable data under various conditions. By integrating AI design with advanced screening, we’ve reduced the design-build-test-learn cycle to just one month. We efficiently evaluate enzyme performance across different criteria tackling the challenge of multi-property optimization.\n\nAt Pando, we help pharma companies improve existing enzymes and discover new ones, cutting costs and boosting productivity. We are also developing our own proprietary enzyme assets.\n\n### Our Team\n\nWe are experts in the field. @Will joined the synthetic biology unicorn Ginkgo Bioworks as an early employee. @Yang spent 6 years at Harvard Medical School before joining Ginkgo. AI-guided enzyme engineering is in our wheelhouse.\n\n### Want to be part of it?\n\nIf you are from a **pharma, diagnostic, or biotech company** and are interested in taking enzyme engineering to the next level, please don't hesitate to reach out to us at [founders@pando.bio](mailto:founders@pando.bio).\n\nThank you!\n\n![uploaded image](/media/?type=post&id=69169&key=user_uploads/875330/303f90f4-0887-4557-92b3-47f2bd20a39f)\n\n",
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    "long_description": "Pests and pathogens cost the world hundreds of billions of dollars every year. Existing technologies like agrochemicals are no longer working. Ohmic Biosciences uses protein engineering to design resistance genes for crops that are robust to pathogen evolution.",
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        "full_name": "PJ Steiner",
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        "founder_bio": "I'm a plant biochemist with experience in protein engineering, genetics and analytical chemistry. I studied Horticulture Science (BS), and then Plant Breeding, Genetics, and Biotechnology (PhD) at Michigan State where I characterized enzymes in tropane alkaloid production for nerve agent antidotes and stimulants (including cocaine). That work led me to a DARPA protein engineering project where I met my cofounder, PJ, and we decided to use our skills to tackle crop disease resistance.",
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        "body": "**TL;DR:** Ohmic Biosciences genetically engineers plants to make them resistant to disease. Our first product is a soybean resistant to a parasite that causes over $1.5B of yearly damage in the US alone.\n\n**The Problem**\n\nPlant diseases cost the world more than $200B every year. Climate change and trade globalization are accelerating the problem, but existing solutions are failing. Today, crop diseases are controlled in two ways:\n\nAgrochemicals are slow and expensive to commercialize. Once pathogens evolve resistance to them, they are useless. Many are being taken off the market and it is increasingly difficult to get new products approved.\n\nGenetic resistance traits are a better solution - no spraying - but are plagued by a basic tradeoff: they usually either work really well but are easy for pathogens to evolve around, or are robust to pathogen evolution but don't work so well.\n\nThis comes down to a basic limitation of plant breeding and biotechnology as practiced today: both amount to hoping the gene you want already exists in nature and embarking on a fishing expedition to find it. Sometimes that works, but there's no reason to believe that genes useful for industrial agriculture will have evolved naturally.\n\n**The Solution**\n\nOhmic Biosciences is taking a different approach. We are protein engineers. We know what traits we want; instead of hoping nature already made them, we're engineering them ourselves.\n\nPlant pathogens have a difficult job. To even start infecting a plant they need to breach the plant's tough cell wall and disable its innate immune system. They do all of this by secreting a whole suite of effector proteins that attack the plant in various ways. We engineer proteins that inhibit those effectors, effectively defanging plant diseases.\n\nWe're starting by focusing on soybean cyst nematode (SCN), a tiny parasitic worm that has completely infested the US and causes >$1.5B in yield loss every year. That number is probably an underestimate, because SCN is an insidious root parasite that rarely causes visible above-ground symptoms - just yield loss.\n\n![uploaded image](/media/?type=post&id=73380&key=user_uploads/1293563/79777d2d-a15c-4669-9d68-5cf2a36c8fcf)\n\n_Above: soybean cyst nematode infection on soybean roots._\n\n**About Us**\n\nWe met as postdocs at the University of Colorado Boulder where we worked together for 2.5 years on [**engineering plant hormone receptors**](https://www.nature.com/articles/s41587-022-01364-5) into biosensors. Before that, PJ worked in CS and synthetic biology and Matt worked in plant genetics and biochemistry.\n\n**Ask**\n\nPlease get in touch if you or anyone you know is interested in protein engineering for agriculture! Find us on LinkedIn [here.](https://www.linkedin.com/company/ohmic-biosciences/)",
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        "created_at": "2023-07-28T19:41:51.431Z"
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        "title": "💧🧬Cleancard - Making cancer screening as easy as a pregnancy test",
        "tagline": "Combining SynBio and AI to make rapid and reliable at-home cancer testing possible",
        "body": "**Tl;dr:** We’re Cleancard, a pair of Rhodes Scholars on a mission to reinvent cancer diagnostics. We combine synthetic biology + AI to create powerful at-home cancer tests, with results from urine in just 30 minutes. **Check out our **[**website**](https://www.cleancard.bio/)** for more!**\n\nHey everyone - we’re Tom and Luca, the co-founders behind Cleancard!\n\n![uploaded image](/media/?type=post&id=73949&key=user_uploads/863380/9ea23e71-c6d5-4715-8236-7ed89e1d6a90)\n\n👨🏻 [Tom](https://www.linkedin.com/in/thomas-carroll-62a60367/): Clinical cancer researcher, expert in bioinformatics and methods development. No stranger to designing [better biomarker tests](https://www.cell.com/cancer-cell/fulltext/S1535-6108(23)00216-7). 👨🏻‍🦰 [Luca](https://www.linkedin.com/in/luca-springer-b3801b8a/): Computer scientist with a strong healthcare policy background. Years of experience growing healthtech and consumer businesses.\n\n### **Early cancer detection saves lives 🕑**\n\nMore than 18M Americans have cancer today. Unfortunately, this is a growth market (26M projected by 2040). 1 in 3 have a close family member with a history of cancer, and thus have a higher risk for cancer themselves (2x). We know these realities all too well from our personal lives, dealing with hereditary cancer syndromes and family histories of cancer.\n\nCancer will come for many of us. To give ourselves drastically better odds of surviving this disease, we need to diagnose it before it reaches an advanced stage.\n\n![uploaded image](/media/?type=post&id=73949&key=user_uploads/863380/10066d6b-22e6-4e98-a5cf-4fc34047d766)\n\n### **Problem: Cancer screening is failing us 🤬**\n\n* Cancer symptoms are often unnoticeable until the cancer is too advanced to cure\n* Current lab-based screening tests are **time-consuming** and **costly** - they fail to get people to screen before they are symptomatic (even for PSA, only [1 in 3 participate](https://jamanetwork.com/journals/jamaoncology/fullarticle/2797845))\n* At-home rapid tests would increase rates of asymptomatic screening but **just don’t perform well enough** to be trusted for cancer diagnostics\n* Current options are **not sufficient** to give people their best chance at surviving cancer\n\n### **Solution: Cleancard’s rapid testing platform 😍**\n\nWe’ve built a new rapid-test method enabling highly sensitive biomarker-guided diagnostics from the urine in just 30 minutes. Key advantages of our proprietary technology:\n\n* **10x quicker/cheaper** than lab-based tests\n* **10x more sensitive** than lateral flow\n* **No expensive machinery** or **complicated sample manipulation**.\n\nSee how we stack up:\n\n![uploaded image](/media/?type=post&id=73949&key=user_uploads/863380/ed2218af-7627-4f5b-9d17-7adec46c6632)\n\nCleancard technology combines synthetic biology, machine learning, and computer vision into a multiplexable, machine-readable format which allows us to detect multiple cancer biomarkers from a single sample.\n\nOur first rapid at-home screens will identify prostate, bladder, and ovarian cancers. Our method is highly agile - we’ve successfully targeted 10 new cancer biomarkers during this YC batch alone, with more to come soon. \n\nCleancard’s platform is the one-stop shop for reliable detection and tracking of multiple cancer biomarkers in the comfort of your own home.\n\n### **Our asks 🙏**\n\n* **Share this post!** Please help us spread the word with your networks.\n* **Join our team!** We are always looking for highly motivated scientists and engineers to join us in our mission to reinvent cancer diagnostics. If that’s you or someone you know, please get in touch ([contact@cleancard.bio](mailto:contact@cleancard.bio)).",
        "url": "https://www.ycombinator.com/launches/JEj-cleancard-making-cancer-screening-as-easy-as-a-pregnancy-test",
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        "title": "Maven Bio - Your AI analyst for BioPharma market intelligence",
        "tagline": "Empowering BioPharma professionals to get more done by pairing AI-powered research with a world-class BioPharma database. ",
        "body": "**TL;DR:** Maven Bio’s AI Analyst can construct a full competitive landscape, research specific drugs or companies, or analyze trial data, all by leveraging our curated content library of over 10 million BioPharma documents. Join hundreds of users from the world's top BioPharma companies, consultancies, and venture capital firms and try our robust free tier at [mavenbio.io](http://mavenbio.io)\n\n**🎯 The Problem:**\n\nMore than 90% of clinical-stage drugs fail - yet while scientific failures get most of the attention, \\~20% of all programs actually fail for business and commercial reasons. \n\nWhy? \n\nToday, therapeutic opportunities are evaluated using a highly manual process with fragmented information along with a heavy dose of consultants, hundreds of slides, and dozens of disparate data sources, which makes it difficult and time-consuming to assess the commercial potential of a drug.\n\n**✨ Our Solution**: \n\nMaven Bio’s AI Analyst for BioPharma Market Intelligence empowers teams to get smart on an opportunity area and understand the competitive landscape faster and more effectively.\n\nUnlike a generative chatbot or traditional database that provides rows of data but takes no action, Maven Bio’s AI Research Analyst **_actually performs analysis for you_**, giving you meaningful insights on critical BioPharma business questions like:\n\n* _What is the competitive landscape, and how differentiated is a given drug compared to current treatments?_\n* _What are the different approaches companies are taking to develop drugs for this disease, and how do they compare?_\n* _How are patients treated today, and what are the gaps in treatment and the level of unmet need?_\n\nIn a field where accuracy is crucial, we leverage our proprietary content library of over 10 million BioPharma documents with a unique combination of both financial and scientific information, including drug and trial data, press releases, SEC filings, earnings transcripts, and much more to provide unrivaled breadth and depth.\n\n<https://youtu.be/eG2awolu3cM>\n\n👋 **Our ask: How you can help** \n\n* Join hundreds of users across top BioPharma companies and try it yourself at [mavenbio.io](http://mavenbio.io) - we have a robust free tier!\n* Connect us with relevant professionals in BioPharma, including: \n  * Consultants focused on the BioPharma space\n  * Business development, corporate development, and new product planning roles at BioPharma companies\n  * Investment professionals (bankers, VCs, etc.) focused on Biotech\n\nIf you want to discuss additional customizations available in our Enterprise plan, or just want to chat, connect with us [here.](https://cal.com/team/maven-bio/intro)\n\n![uploaded image](/media/?type=post&id=85261&key=user_uploads/89898/4d362486-e4bb-4cfb-ac0a-0077cc49171f)\n\n",
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        "full_name": "Emily Damato",
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        "full_name": "Madeline Schade",
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        "id": 82415,
        "title": "MantleBio - Helping scientists transform data into discoveries",
        "tagline": "Modern data engineering, now available free for the scientific community",
        "body": "TL;DR: [Mantle](https://mantlebio.com/) makes data engineering easy (and sometimes fun) for scientists.\n\n**Today, we are excited to announce the launch of Mantle’s free plan, now available to the scientific community. Get started **[**here**](https://mantlebio.com/get-started/)**!**\n\n# 💻  Why does biology need better data engineering?\n\nBiotech generates very big, very complex data. A single DNA sequencing experiment can create 7 TB of data, and generating high-throughput multi-modal data is becoming the norm. Recent breakthroughs in computer science and ML have incredible potential to accelerate life science research. \n\nElectronic lab notebooks (ELNs), like Benchling, help organize research in the wet lab. However, biotech lacks tools to organize and analyze data from instruments, CROs, and public databases.\n\n**Does this sound familiar?**\n\n* Storing experimental metadata in file paths\n  * Writing regex scripts to parse metadata out of those file paths\n  * Regex scripts break when the file path format changes\n* Running bioinformatics scripts locally on a laptop\n  * Not being able to process data when the computational scientist is on vacation\n  * Losing analyses when the computational scientist loses their laptop on vacation\n* Analyzing datasets one by one in a desktop application\n  * Not being able to compare data between teams because each person made the graph slightly differently\n  * Spending a weekend regenerating all of the graphs consistently, and finding several copy-and-paste errors made on the data that was submitted to the grant application last week\n\nAt Mantle, we think science deserves better.\n\n# 🔬 What can you do with Mantle?\n\n**Instant Insights**\n\nMake decisions quickly and confidently. See everything in one place, in real time.\n\n![uploaded image](/media/?type=post&id=82415&key=user_uploads/876391/0e8966ed-9477-45c2-ba61-408feb3884e8)\n\n**Modern Data Management**\n\nDon’t let unscalable spreadsheets and inflexible databases slow down your research. Scientific research requires rapid iteration and experimentation, and Mantle’s data lake can keep up.\n\n![uploaded image](/media/?type=post&id=82415&key=user_uploads/876391/7aeaa967-f989-4112-9a74-e6b343c4f57a)\n\n**Bring Your Code**\n\nEffectively leveraging computation, data science, and ML separates the next generation of biotech companies from the last. Mantle hosts the infrastructure to help connect no-code users and great-code writers.\n\n![uploaded image](/media/?type=post&id=82415&key=user_uploads/876391/6f1860f2-4570-4fa1-8a14-b4fea6961f0c)\n\n# 🌎 How does it work?\n\nMantle is a data infrastructure layer (the “mantle” 🙂 🌎) for biological data analysis.\n\n![uploaded image](/media/?type=post&id=82415&key=user_uploads/876391/45cbb641-63e7-4475-8f46-35af154abca9)\n\n* **Import** data from ELNs (e.g. Benchling), instruments (e.g. Illumina), CROs, and more\n* **Process** data reproducibly with bioinformatics pipelines and Jupyter Notebooks\n* In a private **data lake**, find, organize, and analyze data of any type and scale\n* **Insights** are accessible to no-code users through real-time visualizations\n* **Data Science & ML** results can be easily shared and traced back to raw data\n\n**Why doesn’t biology use “normal” data engineering tools?**\n\n* **The data is different.** General-purpose tools are designed for consistently structured tabular data. Biology generates data with frequently changing metadata in biology-specific formats. _Mantle’s data lake stores data of any scale, format, or type in a single place._\n* **The ecosystem is different.** Tools that are not designed for biology don’t prioritize integrations with other tools in the science ecosystem. _Mantle has off-the-shelf integrations with standard life science tools._\n* **The processing is different.** Tabular data can typically be processed with simple transformations. Preprocessing genomics data can take dozens of hours and careful parameter tuning. _Custom and off-the-shelf bioinformatics pipelines can be run reproducibly through Mantle, with or without code._\n\n# 👋 Help us close the gap between data and discovery\n\n![uploaded image](/media/?type=post&id=82415&key=user_uploads/876391/e731a73f-ecf6-4bc2-b601-ed42ee8fb6a9)\n\nWe love data engineering, and we love helping scientists. If you’d like to, here are some ways you can help us:\n\n* **Give Mantle a try!**\n  * Get started [here](https://mantlebio.com/get-started/) for free.\n* **Let us know what you think!**\n  * If you want to chat or see a demo, please reach out to me here: [emily@mantlebio.com](mailto:emily@mantlebio.com)\n  * If you would like help debugging a weird AWS error, please reach out to my co-founder here: [madeline@mantlebio.com](mailto:madeline@mantlebio.com) \n  * We are celebrating this launch with a Computation & Biology happy hour at our SF office on August 13. Space is limited; please RSVP [here](https://lu.ma/jraavbdm).\n* **Learn with us!**\n  * Check out our blogs on [bioinformatics engineering](https://blog.mantlebio.com/p/intro-to-bioinformatics-engineering), [ELNs](https://blog.mantlebio.com/p/eln-breakdown-scale-scope-and-science), and [more](https://blog.mantlebio.com/).\n  * Check out our upcoming [workshops and events](https://lu.ma/mantlebio).\n* **Share this post!**\n  * If you enjoyed reading this, your friends might too 😀\n\nThank you!",
        "url": "https://www.ycombinator.com/launches/LRH-mantlebio-helping-scientists-transform-data-into-discoveries",
        "votes": 13,
        "created_at": "2024-07-25T14:07:49.934Z"
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        "title": "🔬 MantleBio: Scaling data driven discovery for biotech",
        "tagline": "Our data platform helps scientists and computational biologists manage and analyze high-throughput experiments",
        "body": "Hi everyone, we’re [Emily](https://www.linkedin.com/in/emily-damato-36932523/) and [Madeline](https://www.linkedin.com/in/madeline-schade/) from [MantleBio](http://mantlebio.com)!\n\nMantleBio is a modern data engineering platform for life science research and computational biology. While software like Benchling helps organize research in the wet lab, MantleBio powers research in the dry lab. Our platform helps biotech companies leverage advances in computational science and machine learning with tools built for biotech.\n\nIf you’re a computational biologist or work with biological data we would love to chat. Contact us at [mantlebio.com](http://mantlebio.com) or [info@mantlebio.com](mailto:info@mantlebio.com).\n\n### The Problem\n\nBiotech generates a tremendous amount of data. High-throughput sequencing and screening have become the norm, R&D groups can generate terabytes of data per day, and biotech is estimated to have the largest volume of data of any industry by [2025](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195).\n\nRecent breakthroughs in computer science and machine learning have the opportunity to profoundly impact scientific research. However, the majority of biotech companies lack the tools or software team necessary to fully utilize these advances and the data they have.\n\nWithout implementing the best data practices, biotech companies are often unable to find past results, reproduce analyses, or integrate multiomic datasets. This can be a bottleneck for future experiments and jeopardize regulatory filings. Meanwhile, generic data management software fails to address the complexities specific to life science. MantleBio bridges this gap, accelerating data driven discovery with tools built for biotech.\n\n![uploaded image](/media/?type=post&id=72941&key=user_uploads/1435929/28c97902-c13b-461a-917e-17858a41d971)\n\n### Our Solution\n\n* Simplify multiomics with centralized data management.\n  * Other systems require scientists to design their own schemas. We collaborate with experts to create domain-aware data models available off the shelf.\n  * Our API, CLI, and UI make datasets easily accessible to no-code users and computational scientists.\n* Automate the analysis of routine workflows. Start a DNA sequencing run, grab coffee, and receive the results in your inbox.\n  * We connect to Benchling, Notion, BaseSpace and other systems to read experimental data from the source. Our API and data transformation pipelines allow custom integrations with even the most difficult laboratory instruments.\n* Create bioinformatics pipelines in minutes, not days.\n  * Computational biologists can create production pipelines directly in MantleBio without managing cloud infrastructure or writing Docker files.\n* Prepare for the next IND filing or FDA audit with reproducible datasets and analyses.\n  * MantleBio automatically records a version history for all datasets. The platform captures inputs, processing, and outputs of all analyses, creating a connected and auditable graph of everything in the computational lab.\n\n![uploaded image](/media/?type=post&id=72941&key=user_uploads/1435929/c5949154-a9ed-49a9-9fa5-37c572dc06ad)\n\n## About Us\n\nMadeline is a software engineer with experience as a full-stack engineer at multiple biotech companies and at Google working on large data pipelines. At GRAIL she developed an EDC that supported one of the largest clinical trials ever performed (over 200k patients), and at Google worked on a system supporting 10 billion queries per day. She studied computer science and computer engineering at UC Santa Cruz, where she did research in large distributed systems.\n\nEmily is a “full-stack computational biologist”. Her software experience includes back-end, full-stack, and machine learning. She has done biology research at MIT in both the wet lab and dry lab, and in industry has worked in drug discovery, cell therapy, diagnostics, molecular dynamics simulations, and patient health records.\n\nEmily and Madeline met while working as software engineers at GRAIL, a cancer diagnostic company. From there they both went to Arsenal Bio, a cell therapy company. They realized these biotech companies had common data management needs, but there was no common tool available. They started MantleBio to make the best data engineering practices accessible to all life science research.\n\n### Want to learn more?\n\nContact us at [mantlebio.com](http://mantlebio.com) or [info@mantlebio.com](mailto:info@mantlebio.com).\n\nWe love helping scientists. If you have questions about your current data practices (e.g.“will this scale?”) we also do free data infrastructure reviews.",
        "url": "https://www.ycombinator.com/launches/IyT-mantlebio-scaling-data-driven-discovery-for-biotech",
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        "created_at": "2023-07-11T17:43:56.253Z"
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    "long_description": "Gleam automates data entry for clinical research sites.",
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        "title": "Nanograb: Deliver mRNA to any cell with AI",
        "tagline": "Announcing NGX1, a platform for designing multi-peptide targeted lipid nanoparticles (tLNPs)",
        "body": "**TLDR** Nanograb is announcing NGX1, a universal navigation system for mRNA drugs to deliver cures for any disease. Moderna proved that mRNA will transform skin cancer care, we expand this to all diseases.\n\n<https://youtu.be/ORfErxU5OKk>\n\nDelivery is one of the most important problems stopping life-changing mRNA drugs reaching millions. There are over 3,000 different cell types in the body, but mRNA drug developers can only hit 1% of them. The problem is so pressing that multiple public biopharma partners are paying for early access to NGX1.\n\n![uploaded image](/media/?type=post&id=115774&key=user_uploads/983241/4aac129e-0ad7-45af-9e54-ea4997a5c9d4)\n\n**Works the first time, every time.** There are more peptide sequences than atoms in the universe. Most approaches to solve delivery rely on screening thousands of formulations. NGX1’s biophysics-informed models achieved >25% hit rates on the first try across different targets in mice (4x improvement in cancer cells, 15x for immune cells).\n\n**We hit any cell, any state.** All cells have receptors. Their combination makes up a unique fingerprint. Most people try and target individual receptors whereas we can target the fingerprint, enabled by multivalent physics. We’re creating new drugs that have never existed before. \n\n**NGX1 solves major scale-up issues upfront.** Scaling up targeted mRNA drugs to millions of people is hard because of complex biomanufacturing requirements. Nanograb circumvents these requirements by utilising short peptides instead of antibodies or other biologics.\n\n**15 years of physics research powers NGX1.** Our team includes the foundational researchers of the multivalent biophysics models powering our platform. Our founding team are scientists from Imperial, Oxford and Cambridge.\n\n![uploaded image](/media/?type=post&id=115774&key=user_uploads/983241/1afa2be9-6b73-405a-9931-042318f53d19)\n\nModerna has already shown that mRNA drugs are the future. Eli Lilly showed that mRNA’s potential to be a one-time solution for high cholesterol could help the entire global population. NGX1 is the first step to realising a future where millions of people can access mRNA therapies.\n\nLearn more at [nanograb.com](http://nanograb.com) or reach out via [platform@nanograb.com](mailto:platform@nanograb.com).",
        "url": "https://www.ycombinator.com/launches/U7K-nanograb-deliver-mrna-to-any-cell-with-ai",
        "votes": 10,
        "created_at": "2026-09-17T15:45:22.879Z"
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        "id": 73562,
        "title": "🧪 Nanograb - Multivalent nanoparticles for next-generation targeted drugs",
        "tagline": "We use AI to generate the best combination of binders to treat different diseases",
        "body": "## 💊 **70% of drugs fail in clinical trials because of poor targeting**\n\nThese drugs need lower dosages due to their side effects. Targeting would minimise these side effects and mean that a smaller, more effective dose could be given to a patient. But there are no existing platforms for creating drugs that will target exactly where they need to be in the body.\n\nFor example in cancer, when scientists try to create targeted drugs, they use molecules like antibodies that attach to the cancerous cell at a single site on a single receptor. Since these cancerous cells have the same receptors as healthy cells, both types are killed by toxic payloads!\n\n![uploaded image](/media/?type=post&id=73562&key=user_uploads/983241/0e15bd9d-4ee0-419f-a114-8311a2bc4fa1)\n\n## 🎯 **Our product allows drugs to be targeted to very specific areas of the body**\n\nWe generate the best combination of binders using AI and a multivalent physics engine. We will do what no other technology is able to do:\n\n* Actively minimise binding to the wrong targets\n* Target new parts of the body that have been previously inaccessible\n\nNanograb’s vision is to transform the therapeutic landscape across the entire healthcare spectrum. Nanograb will be creating new therapies on-demand with next-generation targeting whether it’s [infinitely mutating cancers](https://www.nature.com/articles/s41586-023-05729-x), [incurable neurodegenerative diseases](https://en.wikipedia.org/wiki/Neurodegenerative_disease) or [Disease X](https://en.wikipedia.org/wiki/Disease_X)\n\nOur scalable platform has proof of concept data _in vitro_ and _in vivo_ and our goal is to scale these methods to dozens of different applications.\n\n![uploaded image](/media/?type=post&id=73562&key=user_uploads/983241/9bd664c7-6e5f-4bb5-8ccc-bc6a89f951f3)\n\n## **🧠 Our founding team are experts in the field**\n\n* [**Debesh Mandal**](https://linkedin.com/in/debeshmandal) (CEO) completed PhD in computational biophysics and was the first software hire at FabricNano.\n* [**Shanil Panara**](https://linkedin.com/in/shanilpanara) (_In Silico_ Lead) dropped out of PhD to be our in silico lead, pioneering the deployment of our computational pipeline.\n* [**Christopher Lau**](https://linkedin.com/in/christopher-lau-54279815) (Product Lead) has experience delivering projects in energy, finance and biotechnology and brings a wealth of learnings from many industries.\n* [**Dr. Stefano Angioletti-Uberti**](https://www.linkedin.com/in/stefano-angioletti-uberti-b8319860/) (CSO) is the world leader in our theory having published the foundational papers on multivalency.\n* [**Professor Molly Stevens**](https://www.linkedin.com/in/molly-stevens-81742822/) (Advisor) won the Novo Nordisk Prize 2023 and her research group are recognised as pioneers in the biotechnology space.\n\n![uploaded image](/media/?type=post&id=73562&key=user_uploads/983241/3faaa3cd-9ab7-47c1-8a7b-5fe4792cfa53)\n\n## **📢 Get in touch **[**here**](mailto:hello+launchbf@nanograb.com)** if you can help:**\n\n* We are building partnerships with **biopharma companies** and would love any introductions to **principal scientists** or **business leaders** who want to work with us.\n* If you are a **doctor** or **know any doctors** who would enjoy sitting on **our scientific advisory board,** we want to talk about these areas:\n  * Cancer\n  * Degenerative Diseases (e.g. for internal organs)\n  * Infectious Diseases\n  * Neurological Conditions",
        "url": "https://www.ycombinator.com/launches/J8U-nanograb-multivalent-nanoparticles-for-next-generation-targeted-drugs",
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    "long_description": "SynsoryBio is creating next generation, protein therapeutics that sense where they are in the body and only activate at diseased tissue. This technology platform has the potential to expand the therapeutic window of highly potent drugs and apply to many diseases such as cancer and autoimmune disorders.",
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        "full_name": "Elliot Tague",
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        "full_name": "Nathan Tague",
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        "founder_bio": "I’m a co-founder of SynsoryBio, where we are creating therapeutics that sense-and-respond to disease. I have a background in synthetic biology and protein engineering with a PhD in Biomedical Engineering.",
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        "id": 78311,
        "title": "SynsoryBio: Therapeutics that sense-and-respond to disease",
        "tagline": "Our immune system acts locally to combat disease, why don’t our medicines?",
        "body": "**TL;DR**\n\nAt SynsoryBio, we engineer \"_if-then_\" conditional logic into protein medicines, meaning only _if_ the protein drug senses a disease marker, _then_ it turns on. We are applying this technology to clinically tested, but highly toxic cancer immunotherapies to make them safer and more effective. \n\n**Who we are/Team:**\n\nSynsoryBio was founded by brother scientists, [Elliot](https://www.linkedin.com/in/elliot-tague/) and [Nathan](https://www.linkedin.com/in/nathan-tague-533019198/), who both hold PhDs in biomedical engineering. The founders gained expertise in synthetic biology at Boston University, Harvard Medical School, and industrial biotech, where they authored numerous high-impact journal publications and are inventors on several patent filings. SynsoryBio harnesses this expertise to create next-generation therapeutics that understand where they are in the body and only act at the site of disease. \n\n![uploaded image](/media/?type=post&id=78311&key=user_uploads/1336498/724512c9-7cee-4c6b-ba4f-ea2550ef57d9)\n\n**The Problem:**\n\nThe most potent therapeutics suffer from safety issues and dose limitations, impeding their development and overall efficacy. This isn’t surprising because most therapeutics are delivered systemically and are active throughout the body even though the underlying disease is localized to a certain tissue. For example, immunotherapies to treat cancer show great promise but usually exhibit dose limitations, especially in combination, which prevents the development of potent immune stimulators and/or mitigates clinical efficacy.\n\n \n\n**The Synsory Solution:**\n\nWe are developing next-generation protein therapeutics that have synthetic sensing functions so that they know where they are in the body and only act at disease tissue. Our approach is highly flexible, allowing us to design protein therapeutics that conditionally activate upon sensing virtually any specified biomolecule.\n\n![uploaded image](/media/?type=post&id=78311&key=user_uploads/1336498/66febfad-2442-4c03-be04-050f4149b862)\n\n**Our ask:**\n\n\\-If you know any immunologists or oncologists, we would love to connect with them!\n\n\\-Interested in partnering? Please reach out!\n\n Connect with us, info@synsorybio.com",
        "url": "https://www.ycombinator.com/launches/KN5-synsorybio-therapeutics-that-sense-and-respond-to-disease",
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        "created_at": "2024-02-09T15:34:58.143Z"
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    "name": "Tamarind Bio",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "Tamarind Bio is a website and API which allows scientists to use computational biology tools at scale using a simple interface. On Tamarind, scientists can use ML models like AlphaFold to design and simulate molecules by simply selecting inputs instead of or setting up a high performance computing environment or dealing with DevOps.\r\n\r\n\r\nTamarind is used by tens of thousands researchers in large pharma companies, top biotechs, and academic institutions.",
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        "full_name": "Deniz Kavi",
        "title": "Founder/CEO",
        "founder_bio": "Building great software for life scientists.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/deniz-kavi",
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        "location": "San Francisco, CA, US",
        "type": "Full-time",
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        "role_type": "Full stack",
        "salary_range": "$150K - $250K",
        "equity_range": "0.30% - 0.70%",
        "experience": "Any (new grads ok)",
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        "skills": [
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          "Bash/Shell",
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          "React",
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      },
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        "id": 69561,
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        "url": "https://www.ycombinator.com/companies/tamarind-bio/jobs/Sfd2dcu-computational-biologist-protein-engineering",
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        "location": "San Francisco, CA, US",
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      },
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        "id": 86595,
        "title": "Application Scientist - Computational Chemistry",
        "url": "https://www.ycombinator.com/companies/tamarind-bio/jobs/skNUfVc-application-scientist-computational-chemistry",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Science",
        "role_type": "Chemistry",
        "salary_range": "$130K - $200K",
        "equity_range": "0.10% - 0.50%",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
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    "news": [
      {
        "title": "The Startup Building An Operating System For Biotech AI",
        "url": "https://www.forbes.com/sites/the-prototype/2026/02/26/the-startup-building-an-operating-system-for-biotech-ai/",
        "date": "Feb 26, 2026"
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    "launches": [
      {
        "id": 78606,
        "title": "Tamarind Bio: API for scalable bioinformatics",
        "tagline": "The simplest way for computational scientists to deploy and use bioinformatics tools!",
        "body": "**TL;DR**\n\nIn addition to our no-code web platform (<https://www.tamarind.bio/>), Tamarind Bio now offers an API for computational teams to use cutting-edge bioinformatics tools for drug discovery without setting up any computing infrastructure or scaling up GPUs. We provide protein structure prediction (e.g. AlphaFold), protein design(RFdiffusion), and molecular docking tools at large scale and easily integrated into your workflow. Additionally, we deploy your custom models for internal use, provide virtual screening services, and provide pipelines to feed the results of models as input to the next downstream tool. \n\n**The Problem**\n\nComputational biology thrives on doing in silico experiments at large scales. However, it’s tedious to allocate tens/hundreds of GPUs for compute-heavy machine learning tools, make sure they are running as intended, and analyze disparate results. Whether it’s AlphaFold for protein structure prediction, RFdiffusion for binder design or DiffDock for small molecule docking, deploying these tools scalably and connecting them to downstream pipelines steals time away from developing models and doing better science.\n\n**The Solution**\n\nTamarind is launching an API for state-of-the-art computational tools in protein design, structure prediction, and docking. Simply submit your job and check back to receive your result. Download these results, keep them in S3, or generate an analysis. \n\nTamarind now integrates seamlessly with existing computational workflows: we’ll work with you to deploy your custom models and pipelines or run on your existing cloud computing infrastructure. \n\n![uploaded image](/media/?type=post&id=78606&key=user_uploads/1224836/df4f5292-e7ca-44a4-af61-b051397dbb46)\n\nWe are [Deniz](https://linkedin.com/in/deniz-kavi) and [Sherry](https://linkedin.com/in/sherryliu987)! We met as undergrads at Stanford, where we studied Computer Science and conducted comp bio research, experiencing the inefficiencies of using bioinformatics tools firsthand. Today, 1000+ scientists from institutions including Stanford, Harvard, and Oxford regularly use Tamarind to accelerate their work, as well as many YC companies. \n\nLet us handle the DevOps, while you focus on your science!\n\n**Asks**\n\nTry Tamarind out! Use our web platform now or email us to learn more about using the API. Documentation: <https://www.tamarind.bio/api-docs>\n\n* Computational Scientists at small/medium biotech companies in Drug Discovery, Protein/Enzyme Engineering, and Virtual Screening | + adjacent fields like Antibody Discovery\n* Software engineers deploying bioinformatics models\n* Anyone who might benefit from easy-to-use bioinformatics or cheminformatics\n\n![uploaded image](/media/?type=post&id=78606&key=user_uploads/89898/2bf93fd2-1a56-4d34-ab0c-85c5680f6212)\n\nFollow us on [LinkedIn](https://www.linkedin.com/company/tamarind-bio/) to stay updated! Get in touch: [founders@tamarind.bio](mailto:founders@tamarind.bio)",
        "url": "https://www.ycombinator.com/launches/KRq-tamarind-bio-api-for-scalable-bioinformatics",
        "votes": 24,
        "created_at": "2024-02-21T18:24:43.210Z"
      },
      {
        "id": 77001,
        "title": "Tamarind Bio: No-code bioinformatics for scientists",
        "tagline": "The simplest way for life scientists to use computational tools!",
        "body": "### **TL;DR**\n\nWe allow scientists to use cutting edge computational tools for drug discovery without writing any code. We currently provide protein structure prediction (AlphaFold/ColabFold), molecular docking and protein design resources. And would be excited to learn more what computational tools might be useful for scientists.\n\nWe also provide the infrastructure to use these open-source tools at large scales, including efficiently producing large numbers of protein structures along with protein design pipelines.\n\nCheck out our tools here: [tamarind.bio/app](https://www.tamarind.bio/app)\n\n### The Problem\n\nWhether [AlphaFold](https://www.nature.com/articles/s41586-021-03819-2) for protein structure prediction, [RFdiffusion](https://www.nature.com/articles/s41586-023-06415-8) for binder design or [DiffDock](https://www.mlsb.io/papers_2022/DiffDock_Diffusion_Steps_Twists_and_Turns_for_Molecular_Docking.pdf) for small molecule docking; open source machine tools are massively useful for the life sciences. However, these tools remain inaccessible to the scientists who would benefit most from them, due to lack of technical expertise or in-house computational resources.\n\n### The Solution\n\nInstead of implementing tools yourself:\n\n![uploaded image](/media/?type=post&id=77001&key=user_uploads/1224836/2986e780-9df4-4581-bf7a-3f33686e2668)\n\nUse Tamarind:\n\n![uploaded image](/media/?type=post&id=77001&key=user_uploads/1224836/fe2a2ae7-93c9-47dd-9db9-6c800878a0f1)\n\n[Tamarind](https://www.tamarind.bio) is a website to use the state of the art computational tools in protein design, structure prediction and docking.\n\nWe met as undergrads at Stanford, where we studied Computer Science and conducted research, experiencing the inefficiencies of using bioinformatics tools firsthand. Today, hundreds of scientists from institutions including Stanford, Harvard and Oxford regularly use Tamarind to accelerate their work.\n\nNo more Linux terminals or outsourcing computational work, just focus on your science!\n\n### Asks\n\nTry [Tamarind](https://www.tamarind.bio) out!\n\nIf you have or know anyone with the following backgrounds, please get in touch at [founders@tamarind.bio](mailto:founders@tamarind.bio). We’re very excited to learn about your work.\n\n* Scientists in Drug Discovery - industry and academic\n* Computational biologists in Structural Biology\n* Anyone who might benefit from easy to use bioinformatics\n\nFollow us on [LinkedIn](https://www.linkedin.com/company/tamarind-bio/) to stay updated!",
        "url": "https://www.ycombinator.com/launches/K1x-tamarind-bio-no-code-bioinformatics-for-scientists",
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        "created_at": "2023-12-19T23:01:57.000Z"
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        "full_name": "Michael Fester",
        "title": "Founder",
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        "is_active": true,
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        "url": "https://www.ycombinator.com/companies/gloglo/jobs/8U7Ud7v-founding-research-scientist-biology",
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        "role": "Science",
        "role_type": "Biology",
        "salary_range": "$100K - $200K",
        "equity_range": "1.00% - 2.00%",
        "experience": "Any (new grads ok)",
        "visa": "Will sponsor",
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        "title": "Research Scientist Intern Biology (housing provided in San Francisco)",
        "url": "https://www.ycombinator.com/companies/gloglo/jobs/QcxwEJT-research-scientist-intern-biology-housing-provided-in-san-francisco",
        "location": "San Francisco, CA, US",
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      {
        "id": 98342,
        "title": "14.ai: The AI-Native Customer Service Agency",
        "tagline": "The customer service agency for B2C companies. Your entire customer support operation, one line on your expense sheet.",
        "body": "\n![uploaded image](/media/?type=post&id=98342&key=user_uploads/72181/7afbff3c-0ba0-4254-8473-0810886e91fa)\n\nHi everyone, we’re [Marie](https://www.linkedin.com/in/marie-schneegans/) and [Michae](https://www.linkedin.com/in/michaelfester/)l, and we’re building [14.ai](http://14.ai): the AI-native customer service agency. Our customers include AI glasses company Brilliant, men’s health supplement company Sperm Worms, Nordic lighting company Creative Lighting, and luxury skincare brand Yon-Ka. Michael previously co-founded Snips, an AI voice platform acquired by Sonos.\n\n**The problem: the death loop of customer service**\n\nWe’ve all been there: you have a simple problem that you take to customer support, but you’re forced into a never-ending cycle of being passed between different agents and explaining your situation for the (n + 1)th time.\n\n**1. The burden of effort:** If a customer is working harder than the company to solve a problem, the system has failed.\n\n**2. Fragmentation:** You start a chat on your phone, move to your desktop, and have to explain your account number and issue for the third time.\n\n**3. Dumb automation:** You talk with a chatbot that acts as a gatekeeper, not a helper.\n\n**4. Costly, slow onboarding:** Onboarding a new customer support agent brings an expensive and time-consuming burden.\n\nTraditional BPOs (Business Process Outsourcing) try to solve this through headcount in low-cost regions, and rely on manual processes and legacy CRMs that are slow to adapt and prone to error. This approach does not work.\n\n### **The solution: [14.ai](http://14.ai) is the AI-native customer service agency**\n\nWe are a full-service agency built from the ground up with AI as the core engine.\n\n#### **1. High-touch concierge**\n\nExcellent service guides everything we do. After our customers hand over their existing integrations, we tell them to stop answering tickets; now, it’s our problem. This level of service extends to our customer’s customers. We offer the best customer service in the world, and we are always working to stay that way.\n\n#### **2. Low constant onboarding**\n\n[14.ai](http://14.ai) goes live within hours. We take away the burden of onboarding new customer support agents and begin answering your tickets immediately. We get our customers to inbox zero on day zero.\n\n#### **3. Agentic resolution**\n\nMost bots just provide “how-to” links. [14.ai](http://14.ai) actually does the work. We plug in to your company’s most complex processes, autonomously verifying purchases, generating shipping labels, and triggering refunds in a single, seamless flow.\n\n#### **4. Outcomes, not tools**\n\nOur AI engineers operate our own highly tailored software for automation. Customers only see the parts they actually need to see: reports, CX metrics, resolved conversations, VIP or high-touch integrations, and the like.\n\n#### **5. The human-AI feedback loop**\n\nOur human team is supercharged, not replaced. We also service our customers around the clock, 24/7.\n\nOur full-time AI engineers are based in San Francisco and are responsible for answering every support ticket, automated or not. Every time we encounter an edge case, that intervention is fed back into our agents - they become smarter every day.\n\n### **Who is this for?**\n\n**E-commerce companies, mobile applications, fintech companies, healthcare companies, and more.** These are types of companies we’ve worked with already and have seen huge impact with. Customer relationships begin with a 7-day free trial, allowing for onboarding and for potential customers to confirm that there is a fit.\n\nEvery customer we have onboarded repurposed their support teams before their trial even finished.\n\nWe thrive where volume is high and customer support is managed by a number of human customer support agents, either domestically or overseas. If you’re a company with a backlog of support tickets, even better: we’ll take care of your entire backlog on Day 1.\n\n### **Our ask**\n\nWe want to be your customer service. If you’re tired of managing a fragmented support team, fighting with legacy BPOs and VAs, or having a backlog of support tickets, we’d love to talk.\n\n**Visit our website at[ ](https://14.ai)[14.ai](http://14.ai) or book an intro call with us here.**\n\n### **Also! We’re launching the world’s first autonomous consumer brand: GloGlo**\n\n[GloGlo](https://gloglo.com/) are rapid glucose gummies designed for Type 1 diabetics, high-performance athletes, and more. They are built to deliver rapid and reliable carbohydrates, meeting quality-of-life pain points that glucose tablets don’t address and elevating the consumption experience from existing options. The brand is powered by [14.ai](http://14.ai), meaning we are owning and operating GloGlo from the ground up. GloGlo is a blueprint for how B2C companies can operate at a world-class level autonomously, while delivering the best possible customer experience. Check it out at [gloglo.com](http://gloglo.com).\n\n![uploaded image](/media/?type=post&id=98342&key=user_uploads/72181/4e04b858-df53-4a09-a82b-7edaace7ce4b)\n\n",
        "url": "https://www.ycombinator.com/launches/PaA-14-ai-the-ai-native-customer-service-agency",
        "votes": 14,
        "created_at": "2026-03-02T18:23:05.169Z"
      },
      {
        "id": 76470,
        "title": "Markprompt – AI for customer support",
        "tagline": "Close customer support tickets faster with AI",
        "body": "Hi YC!\n\nWe’re [Marie](https://twitter.com/marieschneegans) and [Michael](https://twitter.com/michaelfester) from [Markprompt.](https://markprompt.com)\n\nWe are building AI infrastructure to power customer support at scale. We are live with companies like AngelList, Mindbody, and Plotly.\n\n![uploaded image](/media/?type=post&id=76470&key=user_uploads/72181/65f672d8-ad2b-4f01-843a-75653e4010ab)\n\n**Problem**\n\n* **Scaling customer support with headcount is expensive:** Human resources account for 90%+ of customer support expenses.\n* **Customer support agents burn out:** Support tasks incur large amounts of manual, repetitive, and ungratifying work that humans don't need to be doing, causing burnout, turnover, and reduced quality.\n* **Customer support is more than a chatbot on a website:** In an API-driven world, customer support tools need to evolve with the company’s tech stack. Existing AI-powered solutions are not delivering on the promise, resulting in sub-par customer experience and low NPS and CSAT scores.\n* **Company resources are siloed across systems:** As companies grow, so does their expert knowledge. It is hard to access at scale, and keeping employees up-to-date is nearly impossible.\n\n**Solution**\n\n* **Comprehensive data indexing:** Markprompt addresses the root of the information access problem by indexing all available knowledge within a company, setting the foundation for a robust system of record to feed the AI. This includes:\n  * Salesforce Knowledge databases\n  * Salesforce and Zendesk tickets\n  * Notion wikis\n  * GitHub repositories, discussions, and issues\n  * Company websites, docs, guides, blogs\n* **An API-first approach:** Markprompt is built from the ground up with an API-first approach. Every feature, from indexing and processing of data to generating drafts, triaging tickets, creating chat replies, or triggering automations, can be achieved via our APIs. Markprompt also provides SDKs (such as React hooks and components) and no-code tools to enable integrations at all touchpoints in the customer journey.\n* **Deep integrations:** Markprompt works well within existing support platforms, such as Zendesk and Salesforce. And with our APIs, it is possible to build custom integrations to accommodate any setup.\n\nHere is an example of a bespoke ticket deflection and triaging system built with our React libraries:\n\n![uploaded image](/media/?type=post&id=76470&key=user_uploads/72181/52faa2d8-f529-4ce4-8314-a3f6e5cba849)\n\n**How we help**\n\n* **Efficient ticket deflection:** Deploy AI-driven chatbots and ticket deflection systems that efficiently handle routine queries, allowing support teams to focus on more complex cases.\n* **Accelerated case resolution:** Use automated drafts to streamline case resolution, ensuring faster response times based on the up-to-date expert knowledge.\n* **Actionable AI agents:** Enable AI agents to perform user actions, such as processing refunds or canceling a subscription, with high accuracy.\n\n**Who we are**\n\nWe are life partners, and passionate about design and AI. Michael previously founded Snips (acq. by Sonos), a privacy-respecting voice AI platform that now powers all voice interactions on Sonos speakers. Marie and Michael previously founded [Motif](https://motif.land), a collaborative docs platform based on Markdown and React. While building Motif, we realized that the tasks of maintaining company expert knowledge up to date and providing stellar customer support are intimately connected, and both can in large part be automated. This led us to build Markprompt.\n\nWe are backed by founders and operators at Algolia, Microsoft, Snowflake, Stripe and Vercel.\n\n![uploaded image](/media/?type=post&id=76470&key=user_uploads/72181/19f7c2ed-c74f-4e4a-b959-454f7645b039)\n\n**Our asks**\n\n* **Introductions:** We're looking for tech-savvy companies processing 1000+ customer support tickets per month. Please let us know if you have connections to these companies, especially folks leading Eng or Customer Support. Please introduce us via [founders@markprompt.com](mailto:founders@markprompt.com).\n* **Book a demo:** [here](https://calendly.com/markprompt/30min).\n\n ",
        "url": "https://www.ycombinator.com/launches/JtO-markprompt-ai-for-customer-support",
        "votes": 30,
        "created_at": "2023-11-28T15:00:08.419Z"
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    "name": "ACX",
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    "all_locations": "New York City, NY, USA",
    "long_description": "ACX is developing a new standard for precision agriculture technologies by providing sustainable, eco-friendly alternatives to chemical pesticides. Our mission is to transform agriculture through sustainable biological crop protection that increases yields while safeguarding the environment, farmers, and consumers.\r\n",
    "one_liner": "Next-generation biocides for sustainable agriculture at scale",
    "team_size": 4,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Industrial Bio",
    "launched_at": 1721937425,
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        "full_name": "Emmanouela Petsolari",
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        "founder_bio": "Emmanouela is the Co-Founder & CEO of ACX and PhD in Biochemistry and Biophysics from the University of Cambridge. Previously, she held several research positions working in immunology, cancer, malaria vaccine development and viral drug discovery (in institutes like Barts Cancer Institute CRUK, Cambridge University, King’s College London, Institute Curie France and Imperial College London). ",
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        "linkedin_url": "https://linkedin.com/in/emmanouelapetsolari",
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      {
        "user_id": 1676577,
        "full_name": "Melina Petsolari",
        "title": "Founder/CTO",
        "founder_bio": "Melina is the Co-founder & CTO of ACX and PhD in Computer Science from King's College London. Previously, she held several research positions spanning digital health, robotics, technology-enable interventions, HCAI and NLP (in institutes like Cambridge University, King’s College London,  National Health Service (NHS) in England and Francis Crick Institute).",
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        "linkedin_url": "https://linkedin.com/in/melina-petsolari-a03915298",
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      {
        "id": 82452,
        "title": "🧬🦠 ACX  – Next-generation biocides for sustainable agriculture at scale",
        "tagline": "ACX is developing a new standard for precision agriculture technologies by providing sustainable, eco-friendly alternatives to chemical pesticides.",
        "body": "Hi everyone,\n\nWe are [**ACX**](https://www.linkedin.com/company/acxtherapeutics/)! We’re two PhD’s in Biochemistry/Biophysics and Computer Science from the University of Cambridge and King’s College London developing a new type of therapy with applications in animal farming and agriculture. We have discovered a new molecular weapon to first provide more efficacious pest control measures.\n\n### **Team:**\n\n👩🏼‍🔬[**Emmanouela Petsolari**](https://www.linkedin.com/in/emmanouelapetsolari/) – Molecular Geneticist and Biochemist with 8 years of research experience in drug discovery and development (from leukemia, ovarian, and breast cancer as well as viral and bacterial infections).\n\n👩🏼‍🔬 [**Melina Petsolari**](https://www.linkedin.com/in/melina-petsolari-a03915298/)  – Computer Scientist and Designer with extensive experience in HCI, AI, and healthcare developing hardware and software for applications in digital health, biotech, and privacy.\n\n![uploaded image](/media/?type=post&id=82452&key=user_uploads/1676544/c167d705-6bb6-44bf-86be-5eec9824edc3)\n\n# **❌ Problem:**\n\nThere is more disease impacting our livestock and crops than ever before - and this comes at a time when the human population is also exploding.\n\n1. Manure from livestock and extensive use of limited therapeutics for growth promotion and disease prevention in animal breeding as well as overuse of pesticides in crops\n\n   ●  One billion kilos of chemicals are used annually to combat crop pests\n2. Spread of pathogenic agents that have adapted in previously used medications\n3. Failure of commonly used pesticides and insecticides has been documented in nearly 1000 distinct pest species worldwide\n\n   ●  This leads each year to the world’s food supply facing an up to 30% loss due to insect pests alone, and this number is continuously increasing\n\n# ✅ **Our Solution:**\n\nExisting therapeutics are either no longer effective or have never worked to the extent necessary for achieving positive outcomes. We have discovered a common biological pathway that can be targeted across pathogenic species.\n\n### ✅ ✅ **Specifics:**\n\n1. We have _in vitro_ validation data of our therapeutic showing high inhibition and specificity in the cell line\n2. We are initially focusing on animal health and crop production due to the experimental validation of our therapeutic's effectiveness on a shared pathogen\n3. We are developing drug delivery systems that minimize toxicity and immunogenicity while providing precise spatiotemporal control for targeted drug release\n\n![uploaded image](/media/?type=post&id=82452&key=user_uploads/1676544/1f3bc0eb-72b6-4c8b-80d3-d3172a311f1f)\n\nIllustration of our solution. Nanoparticles carrying the killer compound (orange) infiltrate the target cell like a Trojan horse and activate drug release upon targeting our newly discovered pathway.\n\n### 💡**Key facts about our solution:**\n\n➔   High killing efficacy\n\n➔   Designed to specifically target pathogenic cells while preserving the surrounding healthy cells and ensuring no toxicity or adverse side effects\n\n➔   Highly suitable to become a platform solution (i.e., therapeutic development pipeline expanded to multiple disease areas and organisms)\n\n➔   Scalable, stable, and easy to manufacture\n\n### \n\n# 👋**Our Asks:**\n\n* Share this post with your network and help spread the word!\n* If you’re as excited as we are about this novel approach, email us to learn more about it at [emmanouela@acxtherapeutics.com](mailto:emmanouela@acxtherapeutics.com)\n\n### ",
        "url": "https://www.ycombinator.com/launches/LRs-acx-next-generation-biocides-for-sustainable-agriculture-at-scale",
        "votes": 33,
        "created_at": "2024-07-25T21:52:31.810Z"
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    "id": 29412,
    "name": "Ligo Biosciences",
    "slug": "ligo-biosciences",
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    "website": "https://www.ligo.bio",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We are building the next generation of deep-learning models for enzyme design to slash the cost of chemical manufacturing. The $6 trillion chemical industry is flawed: It produces 20% of industrial greenhouse gases, and is responsible for 15% of global energy usage. \r\n\r\nEnzymes offer a far more sustainable alternative to chemical synthesis and have already revolutionised how a select few chemicals are produced. The problem is each enzyme takes years of trial and error to develop. Our enzyme models learn the principles of catalysis, allowing us to design enzymes for each reaction, in days not years. \r\n",
    "one_liner": "Enzyme design models.",
    "team_size": 4,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Industrial Bio",
    "launched_at": 1723173017,
    "tags": [
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      "Synthetic Biology",
      "Biotech",
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      "America / Canada"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/ligo-biosciences",
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    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/ligo-biosciences/about/?viewAsMember=true",
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    "github_url": "https://github.com/Ligo-Biosciences/AlphaFold3",
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      "name": "Diana Hu",
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        "user_id": 1373049,
        "full_name": "Edward Harris",
        "title": "Founder",
        "founder_bio": "Deep learning for enzyme design!",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/edward-harris-537837188",
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        "user_id": 1451202,
        "full_name": "Emily Egerton-Warburton",
        "title": "Founder",
        "founder_bio": "I'm a biochemist from Oxford University with experience in the intensive environment of biotech startups, working on everything from bacterial biofuel production to vaccine design. Now focused on designing enzymes to make the chemical industry more sustainable. ",
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      {
        "user_id": 1447270,
        "full_name": "Arda Goreci",
        "title": "Founder/CTO",
        "founder_bio": "Founder at Ligo (S24). I studied Cell and Systems Biology at Oxford where I became a Google Cloud Research Innovator for my work in computational biology. Interested in deep learning, scaling laws, geometric DL, biomolecular design. ",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/arda-g-a26506118",
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    "jobs": [],
    "news": [
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        "title": "Show HN: An open-source implementation of AlphaFold3 | Hacker News",
        "url": "https://news.ycombinator.com/item?id=41448439",
        "date": "Sep 20, 2024"
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      {
        "id": 83137,
        "title": "Ligo Biosciences - Generative enzyme design for the chemical industry",
        "tagline": "Next generation deep-learning enzyme models for cheaper and sustainable chemical manufacturing. ",
        "body": "**Tl;dr:**\n\n* [Ligo](https://www.ligo.bio/) is using deep learning to design **novel enzymes** to make **chemical manufacturing** cheaper and more sustainable.\n* The three founders met in a synbio lab at **Oxford University** where they decided to take on the **$6 trillion chemicals industry.**\n* Sign up for our [**waitlist**](https://foil-barometer-dc9.notion.site/Ligo-Biosciences-Technical-Waitlist-63a62e2b0f4a4b8dbaa31ce51b572d09?pvs=74) (Big news soon!)\n\n# **The Team:**\n\nHey - we are Ed, Emily, and Arda! We are building Ligo.\n\n![uploaded image](/media/?type=post&id=83137&key=user_uploads/1373049/1cde2313-c3fb-4cc4-a836-3dd991f69ae9)\n\n👨🏻‍⚕️CEO, [**Ed**](https://linkedin.com/in/edward-harris-537837188) — Ed studied **CS at Princeton** before transferring to **Oxford Medical School,** where he worked across three top synthetic biology labs. Ed bootstrapped his first startup at 19 in the food markets of Guadalajara, Mexico, and took it to **$1M in annual revenue.**\n\n👩‍🔬CSO, [**Emily**](https://www.linkedin.com/in/emily-egerton-warburton-a70121211/) — Emily is a top **biochemist** from **Oxford University** who honed her wet lab skills in the intensive environment of biotech startups. Working on everything from bacterial **biofuel** production to **vaccine design**, Emily is **happiest with a pipette in hand**!\n\n👨‍💻CTO, [**Arda**](https://linkedin.com/in/arda-g-a26506118) — Arda studied **cell and systems biology** at **Oxford,** where he became a **Google Cloud Research Innovator** for his work in computational biology. Arda’s obsession with deep learning for biomolecular design began when the original AlphaFold paper was released.\n\n---\n\n---\n\n---\n\n---\n\n---\n\n---\n\n## ❌ **The problem**:\n\n**(1) The $6 trillion chemicals industry is flawed:**\n\n* This industry produces **_20% of industrial greenhouse gases_** and is responsible for **_15% of global energy usage_.**\n* Traditional chemical manufacturing relies on hazardous materials and produces **_huge amounts of waste,_** resulting in **high costs.**\n* **You** unknowingly contribute to this problem by personally using **\\~160 of these chemicals daily.**\n\n**(2) Enzymes offer an amazing solution, but they are hard to develop**\n\n* Enzymes are biological catalysts. They accelerate reactions at mild temperatures and pressures, making chemical manufacturing **cheaper, faster, and more sustainable.**\n* **Major pharmaceutical companies** already use enzymes for manufacturing a limited number of drugs but developing them costs **tens of millions of dollars** and takes **multiple years.**\n* Current enzyme engineering approaches are **limited** because they must start from an enzyme that already exists.\n* No models currently understand the principles of catalysis, resulting in a narrow range of possible reactions that can be catalysed.\n\n## ✅ **Our Solution: Enzymes Designed From Scratch**\n\n* We are building **foundational enzyme design models** that learn from huge amounts of data to **understand the principles of catalysis.**\n* The model **generates structures capable of catalysing reactions** directly from transition state models, meaning we will **expand the number of reactions that can feasibly be accelerated** using enzymes.\n* These enzymes can catalyse reactions to **synthesise high-value chemicals** used in the pharmaceutical, agriculture, and consumer goods industries.\n\n[Our Diffusion Model](https://youtu.be/_V758AisYS8)\n\n# **Partnerships:**\n\nWe are happy to announce two key partnerships.\n\n* [**Basecamp Research**](https://www.basecamp-research.com) is on track to have **1000x more sequence diversity than public resources** - We are collaborating to use their data to improve our soon-to-be-released OpenSource model (sign up [here](https://foil-barometer-dc9.notion.site/Ligo-Biosciences-Technical-Waitlist-63a62e2b0f4a4b8dbaa31ce51b572d09?pvs=74)!).\n* [**Adaptyv Bio**](https://www.adaptyvbio.com) is building a next-generation protein foundry. Their engineers, Liza and Igor, are helping to build the **data pipeline for OpenSource model** using their state-of-the-art **bioinformatics tool, ProteinFlow.**\n\n# **We’d Love Your Help**❤️\n\n* Sign up for our [waitlist](https://foil-barometer-dc9.notion.site/Ligo-Biosciences-Technical-Waitlist-63a62e2b0f4a4b8dbaa31ce51b572d09?pvs=74) (Big news soon!)\n* We’d love intros to:\n  * Small chemical companies in pharmaceuticals, fragrances, food&drink, detergents\n  * Big Pharma and Agricultural chemical companies\n  * **Any** **other biotech** companies! Email us [ed@ligo.bio](mailto:ed@ligo.bio)",
        "url": "https://www.ycombinator.com/launches/Lcv-ligo-biosciences-generative-enzyme-design-for-the-chemical-industry",
        "votes": 470,
        "created_at": "2024-08-16T21:11:49.384Z"
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    "id": 29429,
    "name": "Velorum Therapeutics",
    "slug": "velorum-therapeutics",
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    "website": "https://www.velorumtx.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Velorum Therapeutics is developing breakthrough medicines by unlocking the biology of heme.",
    "one_liner": "Breakthrough medicines unlocking the biology of heme",
    "team_size": 5,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Therapeutics",
    "launched_at": 1706719304,
    "tags": [
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        "full_name": "Pouya Modareszadeh",
        "title": "Founder/CEO",
        "founder_bio": "Co-Founder & CEO @ Velorum Therapeutics. Building new medicines.",
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        "founder_bio": "Co-Founder and President @ Velorum Therapeutics. Goldwater Scholar. Working to create breakthrough medicines for devastating diseases. ",
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        "title": "Velorum Therapeutics: Powerful cancer drugs that starve tumors",
        "tagline": "We're developing a new class of cancer drugs that starve tumors by hijacking cancer metabolism.",
        "body": "### **ABOUT VELORUM**\n\n[**Velorum Therapeutics**](https://www.velorumtx.com/) **is developing a new class of cancer drugs that starve tumors by hijacking cancer metabolism. We have shown our drugs work in mice and plan to begin human clinical trials next year, starting with the #1 cancer killer – lung cancer.**\n\n### **OUR SCIENCE**\n\n**We’ve discovered that cancers are addicted to a molecule called ‘heme’.** Heme, which is well-known as the central component of ‘hemoglobin’, is used by the mitochondria to make energy. Cancers ‘steal’ free-floating heme from the blood to fuel their rapid growth. **Our patented drugs starve cancer by removing this heme that cancer cells need, but normal cells don’t.** We’ve shown in animals that our drugs are highly effective for multiple cancers, without affecting healthy cells, even at the highest doses. Also, by starving tumors, we make them weaker and more susceptible to existing cancer treatments, like radiation, chemo, and immunotherapies.\n\nThe body has a natural system to clean up this free-floating heme, but it gets overwhelmed by cancer. **Our drugs essentially amplify the body’s natural heme removal system.** We let nature do most of the work – our drugs are built from proteins that have evolved over millennia to be perfect at capturing heme. Importantly, heme is the exact same molecule across all mammals, meaning the results we see in animals have a higher chance of translating to humans.\n\nWe’re starting with lung cancer, with a lead drug advancing to first-in-human trials next year. **Because multiple types of cancers have this same vulnerability, our lead drug could potentially be used for lots of cancer types and become a standard-of-care treatment.**\n\n### **OUR TEAM**\n\n[Parsa](https://linkedin.com/in/parsa-modareszadeh) and [Pouya](https://linkedin.com/in/pouya-modareszadeh) are twin brothers and scientists who spent their entire adult lives studying how cancers make energy. They worked together on this breakthrough science over the past 5 years, helping develop this promising new class of cancer drugs. They founded Velorum to advance this breakthrough to patients, surrounding themselves with a team of world-class drug developers, scientists, and clinicians.\n\n![uploaded image](/media/?type=post&id=78069&key=user_uploads/1647572/3e39d62c-27d4-4f86-8e4b-94e9bc70772e)\n\n### **CONTACT US**\n\nTo learn more about our science or support us on our mission, please reach out at [info@velorumtx.com](mailto:info@velorumtx.com)",
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        "body": "**About us**\n\nHello 👋 We’re [Evita](https://www.linkedin.com/in/evita-weagel/) and [Rachel](https://www.linkedin.com/in/rachel-brog-garlick/), co-founders of [Eris Biotech](https://www.linkedin.com/company/eris-biotech). We met working at the same lab at BYU when Evita was a Ph.D. student and Rachel was an undergrad. We collaborated on a peer-reviewed paper and conference presentations, and we’ve been friends since! Rachel went on to Dartmouth for her Ph.D. and later worked at a startup developing cell therapies. Evita went on for a postdoc and a career in drug development in big pharma. Rachel has developed multiple novel therapies for patients and Evita has brought numerous drugs to the clinic. We’re excited to bring our expertise together to build a first-in-class treatment that follows our mission to aggressively fights cancer, but also improve the patient experience.\n\n![uploaded image](/media/?type=post&id=78251&key=user_uploads/1579332/86e96bb5-bf3c-4acc-9041-1efbe0d836a0)\n\n**Our Backstory**\n\nEvita, working in drug development, saw promising cancer drugs fail because they were simply too toxic. Rachel, in cell therapy, saw the same problem as treatments that worked in principle caused too much collateral damage. Both realized that the real issue was not just the drugs themselves but the way most cancer treatments rely on toxic approaches that also wipe out our best weapon against cancer, the immune system. Tumor immune evasion is one of cancer’s greatest strengths, yet few were tackling it head-on. We founded Eris Biotech to change that and to develop therapies that stop tumors from hiding and reawaken the immune system.\n\n**The Problem**\n\nOne of cancer’s greatest weapons is its ability to hide from the immune system. Tumors release billions of **tiny vesicles** that contain “don’t attack me signals” and  shut down the immune system, blinding it to their presence. This immune evasion lets cancer spread unchecked while most current treatments, such as chemotherapy, radiation, and even some immunotherapies, further weaken the body’s natural defenses.\n\n**Our Solution**\n\nEris Biotech develops small-molecule drugs that prevent tumors from forming and releasing immune-suppressing **tiny vesicles**. By stopping this process, we remove cancer’s disguise, restore immune surveillance, and give the body’s natural defenses the upper hand. Essentially, our drug doesn’t kill cancer directly like toxic chemotherapies – instead, our drug awakens the immune system to fight cancer for you. This approach targets one of cancer’s most powerful survival strategies while avoiding the high toxicity of traditional treatments. We’re starting by targeting mesothelioma, a cancer of the lung lining, with a 5-year survival rate under 13 percent.\n\n**Contact us**\n\nIf you want to learn more about our science and mission, you can reach us at [**founders@erisbio.com**](mailto:founders@erisbio.com)",
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      {
        "id": 78838,
        "title": "🎯🦠  Granza Bio – Cancer targeting attack particles derived from our body’s immune system",
        "tagline": "We engineer \"attack particles\" that selectively target and kill cancer cells, eliminating unwanted side effects and toxicity.",
        "body": "**Tl;dr:** We are [Granza Bio](https://www.linkedin.com/company/granza-bio-yc-w24/), two PhD graduates in Oncology and Immunology from the University of Oxford on a mission to develop personalised and targeted cancer treatments. We are working on a new weapon in our immune system’s arsenal – “[**attack particles**](https://www.science.org/doi/10.1126/science.aay9207)”. We are turning this concept into a therapeutic product that can eliminate any cancer without harming the surrounding healthy cells, preventing adverse side effects.\n\nHey everyone, we are (Ashwin)²\n\n![uploaded image](/media/?type=post&id=78838&key=user_uploads/1718918/c608a07f-d480-40c7-bb19-16fc620b9666)\n\n# **Team**\n\n🧑🏾[Ashwin Jainarayanan](https://www.linkedin.com/in/jainarayananak/) – Clarendon Scholar and Synthetic Immunologist with extensive research experience on attack particles derived from immune cells.\n\n 🧑🏾[Ashwin Nandakumar](https://www.linkedin.com/in/ashwin-nandakumar-menon/) – Cancer biologist specializing in immuno-oncology, who has previously set up clinical trials and brings extensive Oncology start-up experience.\n\n👨🏻‍🔬 [Mike Dustin](https://www.kennedy.ox.ac.uk/team/michael-dustin) – Professor of Molecular Immunology at the University of Oxford. Widely recognised for his work on immunological synapses and discovered attack particles.\n\n# **❌ Problem:**\n\n\\~50% of cancer patients receive treatments that are (1) ineffective and (2) lead to unwanted and avoidable side effects.\n\n**(1) _High mortality rates:_** Ineffective treatments lead to cancer relapse and ultimately, increased likelihood of dying from cancer. Five-year survival rates are lower than 5% for patients with late-stage cancers including lung, pancreas, and brain cancer.\n\n**(2) _Toxicity and adverse side effects_**: While emerging treatments such as immunotherapy and CAR-T cell therapy are effective in some patients, they also impact their healthy tissues. This causes high toxicity and adverse side effects.\n\n# **✅ Solution: Granza Bio’s cancer-targeting attack particles**\n\nWe have identified **attack particles** ⚡: a powerful suite of weapons in our immune system’s arsenal. These attack particles carry proteins like **_granzymes_** and **_perforins_** that instantly kill cancer cells.\n\n[Watch how it works!](https://www.youtube.com/watch?v=f5IPz1YaAiE&ab_channel=AshwinMenon)\n\n# **📋 Factfile: Granza Bio’s attack particles**\n\n* **💀 High cancer-killing efficacy** in brain, ovarian, lung, and skin cancers.\n* ⚙ Engineered to target **only the cancer cells**, leaving the surrounding healthy cells untouched, meaning no toxicity or adverse side effects.\n* 🧠 Capable of targeting **brain cancers** by crossing the blood-brain barrier.\n* **🎯** Perfectly suited to a **platform approach** meaning we can use attack particles to target a great range of diseases including cancers, autoimmune diseases, and infections.\n* 🏭 **Highly stable** and easy to manufacture.\n\n**Lock & Key method – attack particle targeting cancer**\n\n# \n\n![uploaded image](/media/?type=post&id=78838&key=user_uploads/1718918/0fcc26f3-2645-4d78-aee9-8287c2e707c2)\n\n# **🙏 Our Asks:**\n\n* Share this post with your network and help spread the word!\n* If you’re as excited as we are about this novel approach to eradicating cancer and would like to learn more about what we’re doing, email us at [founders@granzabio.com](mailto:founders@granzabio.com)",
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        "full_name": "Jose Miguel Hernandez Lobato",
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        "full_name": "Laurence Midgley",
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        "title": "Ångström AI: Gen AI molecular simulations",
        "tagline": "Replace wet lab experiments with gen AI molecular simulations",
        "body": "---\n\n**TLDR:** **We build fast and experimentally accurate generative AI-based simulations of molecular interactions for pharma and biotech companies.** These simulations can tell us whether a drug is going to bind to a protein or how quickly a drug will act once it is consumed by a patient.\n\n---\n\n# Our Team\n\n![uploaded image](/media/?type=post&id=81767&key=user_uploads/89898/2077fd93-2682-4e97-984e-99e64583d67b)\n\nWe are a team from the University of Cambridge with 30+ years of combined experience in AI and Molecular Modelling, including:\n\n* [Javier](https://scholar.google.com/citations?user=_b-Cs2cAAAAJ), who scaled probabilistic AI methods 1000x during his PhD, turned down a research scientist position in Big Tech to start Angstrom AI.\n* [Laurence](https://scholar.google.com/citations?user=vMd1gwMAAAAJ), who developed FAB, a method for training AIs from physical equations without training data, during his PhD and was a researcher at InstaDeep before its acquisition by BioNTech in 2023.\n* [Miguel](https://scholar.google.com/citations?user=BEBccCQAAAAJ), Javier and Laurence's PhD supervisor, author of foundational research on AI for drug discovery and generative modeling.\n* [Gabor](https://scholar.google.com/citations?user=q39javYAAAAJ), who developed MACE, a state of the art quantum mechanically accurate force field.\n\nMiguel and Gabor's work has been cited over 40k times.\n\n# The Problem\n\nDuring drug discovery and development, pharmaceutical companies need to understand how drug molecules interact with other molecules in the human body to determine the drugs’ efficacy and safety.\n\nConventional methods to assess molecular interactions are unsatisfactory:\n\n* Wet lab experiments are accurate but slow and expensive.\n* Machine learning prediction methods, like AlphaFold, are fast but inaccurate. They are limited by the quality and quantity of training data, which must be generated by lab experiments.\n* Molecular Dynamics simulates interactions by rolling out the equations of physics, offering a balance between wet lab and machine learning predictions in accuracy and speed. These factors depend on the model of physics used. More accurate models are more expensive to run. This makes the method compute bottlenecked rather than data bottlenecked.\n\nHere is a video of a simulation of 64 water molecules. <https://www.youtube.com/watch?v=18AuJEVnwWA>\n\n# Solution: Fast and Accurate Molecular Simulations\n\nWe run molecular simulations, keeping us in the compute constrained regime, but we combine **1) quantum mechanically accurate models of physics 2) generative AI that allows us to run these models quickly.**  \n\n1. We use the MACE (multi-atomic cluster expansion) physics model, which accurately reproduces quantum-mechanical interactions. It was developed by Gabor, our co-founder. In collaboration with our academic partners, we recently showed MACE simulations are the **first ever to provide accuracy comparable to lab experiments** when estimating the hydration-free energies (a quantity relevant to drug bioavailability). Below is a plot from the [resulting publication](https://arxiv.org/abs/2405.18171). However, MACE is computationally expensive, each of the results from the below plot required 1 week of compute on 8 A100 GPUs.\n\n   ![uploaded image](/media/?type=post&id=81767&key=user_uploads/649736/7ff4d768-80aa-4c10-b89a-1960aa64ce69)\n\n2. We use diffusion models to accelerate MACE simulations, making them computationally affordable. Our models generate states **consistent with physics, but the transitions between states are non-physical and significantly faster.** Here is a video of our AI simulating the same water box as above.\n\n<https://www.youtube.com/watch?v=aK1MNLsQg2Y>\n\n# Demos!\n\nHere are a couple of examples to show off the type of stuff you can do with our generative models.\n\n### Speeding up Supercool Water\n\nSupercool water—liquid water below freezing, here at -40°C—is notoriously difficult to simulate with traditional methods because cold molecules move slower. The plot below shows the correlation between water molecule orientations across simulation steps. **Our AI introduces about 10,000 times more information per step compared to traditional simulations.**\n\n![uploaded image](/media/?type=post&id=81767&key=user_uploads/649736/3b1a48bc-da17-4627-ad23-4d491b5be6d0)\n\n### Hydrating Methane\n\nModeling interactions between molecules and water allows us to calculate how quickly the molecules will dissolve and their bioavailability as drugs. Here are videos of a traditional simulation of a methane molecule interacting with water and our AI simulation. **Our AI prioritizes the movement of the methane molecule and its surrounding water, which are the parts that matter for solubility and bioavailability calculations.**\n\n<https://youtu.be/EmTit8tC0TE>   \n\n<https://youtu.be/pq6Al2SATlg>\n\nThese are the first ever **genAI accelerated, physically accurate molecular dynamics simulations** incorporating the interaction of many molecules. We are scaling up - so stay tuned!\n\n# Our Ask\n\nReach out to [info@angstrom-ai.com](mailto:info@angstrom-ai.com) if\n\n* You are in pharma or biotech and interested in learning about the theory of diffusion models and quantum-mechanically accurate models of atomic interactions. We would be happy to give a talk on our research or have a more informal chat over Zoom. \n* You have friends who work in pharma or biotech and are interested in computational methods. We would love to meet them!",
        "url": "https://www.ycombinator.com/launches/LGp-ngstr-m-ai-gen-ai-molecular-simulations",
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        "created_at": "2024-06-26T16:24:53.448Z"
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    "website": "https://www.undermind.ai",
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    "long_description": "At Undermind, we're building a search engine that can handle extremely complex questions. It’s geared at experts, like research scientists and doctors, who need to find very specific resources to solve high-stakes problems. \r\n\r\nWe’ve rebuilt search from the ground up to address this. Our new approach employs high-quality LLMs to adaptively explore a database, mimicking how a human researcher carefully discovers information. This approach dramatically outperforms (by 10-50x) traditional keyword search and other modern AI-based retrieval methods.\r\n\r\nOur first target users are the 50 million researchers searching for scientific literature on PubMed and Google Scholar every month. We’ve have paying users across fields like medicine, ML, biotech, finance, and more.",
    "one_liner": "An AI agent for scientific research",
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        "founder_bio": "Tom is the co-founder and CTO at Undermind. He studied physics at Princeton, then did his PhD in quantum physics at MIT. His thesis was recognized by the American Physical Society as one of the top 4 theses in the field of atomic physics in 2022, and he has published 10 papers in the field, including two in Nature and Science. Tom has deep expertise in building and deploying complex systems.",
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        "title": "🔍 Undermind - Radically better search for scientific research",
        "tagline": "Imagine sending a smart colleague to find exactly what you need",
        "body": "# **📝 TL;DR:**\n\n* [Undermind](https://undermind.ai/1) is Google for scientific research. It finds exactly what you need, no matter how complex, using an AI agent.\n* **Try it for free -** [www.undermind.ai](https://undermind.ai/1)\n\n# **———**\n\n---\n\n---\n\n---\n\n---\n\n# **❌ The Problem:**\n\nFinding the right resources to solve problems is crucial for doing impactful research. However, researchers often need to find really complex information, and tools like PubMed and Google Scholar simply aren’t up to the task. They only work for simple, short keyword phrases, and it often takes hours to sift through the (mostly irrelevant) results.\n\n# **✨ Our Solution:**\n\nWe rebuilt search from the ground up to make it work really well for complex questions. Our AI agent mimics how a human researcher carefully discovers information. It spends a few minutes carefully looking through hundreds of papers for you and adapts based on what it finds to uncover everything. Ultimately **Undermind’s results are 10-50x better** than traditional keyword search engines.\n\n![uploaded image](/media/?type=post&id=82132&key=user_uploads/1394068/4ce1e368-67de-47c7-af78-860c088daf7e)\n\n# **⚙️ How to use it**\n\nJust write down your complex research topic, describing exactly what you want, as if to a colleague. After 2-3 minutes, Undermind will give you the precisely relevant articles you need.\n\n![uploaded image](/media/?type=post&id=82132&key=user_uploads/1394068/8b54d81a-70b5-4001-b0a8-2235cf27e4b2)\n\n# **Why we’re doing this**\n\nWe’re scientists ourselves, and we’ve spent a lot of time looking through scientific literature. We’ve personally experienced those “aha” moments - when you realize you could have saved 3 months of work if only you knew about that one paper earlier! So, our goal isn’t to save you a few minutes of searching yourself; it’s to make it easy to find the solutions that can drastically improve your overall research plan and trajectory.\n\n# **——**\n\n# **🙏 Asks**\n\n* Try Undermind now - [www.undermind.ai](https://undermind.ai/1)\n* **Share** this post with anyone you know who reads research - it’s helpful for anyone, from machine learning to biotech, medicine, and engineering!",
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    "all_locations": "London, England, United Kingdom",
    "long_description": "We design point-of-care diagnostics for emerging biological threats - in days, not 12+ months.",
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        "full_name": "Abhi Rajendran",
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        "founder_bio": "Ex-Mercedes Formula 1 engineer and computational biologist working to fight emerging biological threats",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/abhirajen",
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        "full_name": "Adam Wu",
        "title": "Founder/CTO",
        "founder_bio": "Building the smartest protein engineer in the world.\n\nShameless plug: check out 'adwuu' on spotify for a good time",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/akhwu",
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        "id": 92017,
        "title": "Amina: The Agentic Protein Engineer",
        "tagline": "Design, simulate, and test your proteins 100x faster with our no-code platform for protein engineering!",
        "body": "**👋 Hey YC! We're Abhi and Adam from AminoAnalytica.**\n\n**Amina is the first AI agent for end-to-end protein engineering** – instead of wrestling with hundreds of fragmented tools, scientists can now design protein therapeutics through simple conversation.\n\n## **🎬 Trailer**\n\nhttps://www.youtube.com/watch?v=n8Pd9BJcbQA\n\n## **🧬 The Problem with Programmable Biology**\n\nThe 2024 Nobel Prize for Chemistry went to AlphaFold - proving we can predict and design proteins like software. These biological machines could solve climate change, cure diseases, and manufacture anything we need.\n\nBut here's the reality: **hundreds of new computational biology tools launch every week.** Even expert researchers stick to the same old methods they know, missing breakthrough tools that could accelerate their work by months or years.\n\n**Meet Dave, your friendly neighborhood protein engineer** 👨‍🔬\n\nDave is designing a cancer therapeutic, but his current workflow is broken:\n\n* **Inaccessible tools** → most have no UI, require specific hardware, must be built from source\n* **Fragmented ecosystem** → tools don't work together, requiring pages of glue code\n* **Constant obsolescence** → by the time he masters one tool, three better ones have launched\n\nThe result? **Scientists spend more time debugging code than designing life-saving therapeutics.**\n\n## **🚀 Our Solution**\n\n**What if you could just describe what you want in plain English?**\n\nInstead of wrestling with complex pipelines and staying up-to-date with every new tool, you simply tell Amina what protein you need and why.\n\n**Dave before Amina:**\n\n![uploaded image](/media/?type=post&id=92017&key=user_uploads/1754946/ad201bdd-6ed8-4b33-bb1f-fd8ca58f5262)\n\n**Dave with Amina:**\n\n![uploaded image](/media/?type=post&id=92017&key=user_uploads/1754946/3657d135-74af-4ed9-b261-394f14fe42a5)\n\n## **🔬 What Makes Amina Different**\n\nThis isn't just model hosting. Amina is trained on both scientific literature and our proprietary experience from engineering thousands of proteins for customers - it knows which tools to use when, how to chain them together, and how to interpret results. It's like having our entire computational team's expertise distilled into a single agent.\n\nWith our no-code interface, 50+ specialized tools work together seamlessly. Amina catches potential issues, suggests optimizations, and explains results in plain English.\n\n## **🎯 Our Vision**\n\n**Our ultimate goal is for Amina to design proteins autonomously** - think JARVIS, but dedicated to discovering breakthrough therapeutics. For now, it's like having an extra bioinformatician on your team that needs less care and attention than your cell cultures.\n\n## **🙏 Our Ask**\n\n**If you work in biotech, pharma, or have an interest in protein engineering** - we'd love your feedback on Amina.\n\n**Try it for free at [aminoanalytica.com](http://aminoanalytica.com)** - no setup required, start designing in 60 seconds.\n\nNeed extra credits? Just message us at [support@aminoanalytica.com](mailto:support@aminoanalytica.com)",
        "url": "https://www.ycombinator.com/launches/Nw9-amina-the-agentic-protein-engineer",
        "votes": 15,
        "created_at": "2025-07-09T18:31:25.117Z"
      },
      {
        "id": 82706,
        "title": "🧬 AminoAnalytica – Next generation proteins engineered with *protein* language models",
        "tagline": "We’re creating the next generation of peptides and enzymes using our proprietary pipeline, powered by protein language models.",
        "body": "Hey everyone – it’s [Abhi Rajendran](https://www.linkedin.com/in/abhirajen/), [Adam Wu](https://www.linkedin.com/in/akhwu/) and [Matteo Peluso](https://www.linkedin.com/in/matteo-peluso-872b85199/)\n\n**Tl;dr**: just look at the pictures ;)\n\n# **💞 The A(mino) Team**\n\n![uploaded image](/media/?type=post&id=82706&key=user_uploads/1754946/96f298df-f236-4401-91d5-2ce61024e644)\n\n👨🏾 **Abhi Rajendran** – From Imperial College London to the Mercedes F1 Team, Abhi builds AI tools in data-intensive environments. AminoAnalytica was inspired by his computational bio master’s project and was the top AI x Robotics venture from Imperial in 2024.\n\n👦🏻 **Adam Wu** – With experience in academic and industry wet labs, Adam is now building his own. He has a background in materials science, tissue engineering and biosensing as a Thermo Fisher Scholar from Imperial.\n\n👱🏼‍♂️ **Matteo Peluso** – Bioinformatics PhD from the University of Zurich. His skills in biology, machine learning, and software development tie up everything seamlessly.\n\n👨🏻‍🦱 **Dr. Stefano Angioletti-Uberti** – Senior lecturer at Imperial, specializing in modeling biological and soft matter systems. He ensures models are based on the latest scientific principles.\n\n# **❌ Problem: better proteins are needed to accelerate global innovation**\n\n* Industrial and pharma companies prefer to use enzymes and peptides over harsh chemicals as they are more specific and sustainable.\n* However, natural proteins often lack stability, efficiency, or specificity for their desired applications leading to poor performance\n* Improving these proteins has been a costly and inefficient 'trial-and-error' process, where biotech companies randomly mutate existing proteins, test them, and repeat until success.\n\n![uploaded image](/media/?type=post&id=82706&key=user_uploads/1754946/afb4dbcf-6cc7-43e1-915e-609278e798fa)\n\n# **✅ Solution: AminoAnalytica’s protein adaptation platform, powered by PLMs**\n\n* Like how ChatGPT understands human language, our **\"protein language models\" (PLMs)** understand biology and can optimize proteins for industrial and therapeutic use.\n* This allows us to perform in-silico protein property prediction, eliminating the need for thousands of lab tests.\n* We’re transforming the traditional ‘trial-and-error’ method into an ultra-high throughput and rational process.\n\n![uploaded image](/media/?type=post&id=82706&key=user_uploads/1754946/65fb8e1a-d749-4245-adca-a0abc1ad8529)\n\n# **🤝 Any industry working with proteins can benefit from our tech**\n\nCurrently, we’re working on:\n\n* **Aquaculture feed** → enzymes for maximizing fish feed nutritional efficiency\n* **Pharmaceuticals** → peptide drugs with improved stability and specificity\n* **Climate tech** → enzymes for eco-friendly carbon capture\n* **Consumer goods** → safer enzymes for everyday products\n\n![uploaded image](/media/?type=post&id=82706&key=user_uploads/1754946/30bf87a9-4ee8-49e6-a0ee-7f8dd85bf008)\n\n# **🐡 Get in touch with us!**\n\n* Upvote/share this post with your network and help spread the word!\n* If you (or anyone in your network) are as excited as we are about next generation industrial proteins and would like to learn more about what we’re doing, email us here - [founders@aminoanalytica.app](mailto:founders@aminoanalytica.app)\n* We’re also looking to expand **our advisory board** in the areas of aquaculture, fish testing, and feed manufacturing!\n\n![uploaded image](/media/?type=post&id=82706&key=user_uploads/1754946/908a3520-d234-4338-ad05-154aa3515672)\n\n",
        "url": "https://www.ycombinator.com/launches/LVy-aminoanalytica-next-generation-proteins-engineered-with-protein-language-models",
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        "created_at": "2024-08-04T00:01:55.931Z"
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    "one_liner": "Genetically engineered viruses that teach your immune system to kill…",
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        "full_name": "Alexander Haddad",
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        "founder_bio": " Neurosurgery resident @ UCSF, scientific co-founder @ Kopra Bio. Published over 70 peer-reviewed publications as a neurosurgery resident at UCSF. Alex is particularly interested in the delivery of cell and gene therapies to the central nervous system and neuro-oncology. Kopra Bio is a serial biotech entrepreneur and a UCSF Neurosurgery resident on a mission to revolutionize cancer treatment. ",
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      {
        "id": 83219,
        "title": "🔬🧠🧬 Kopra Bio - Making cancer cures using viral immunotherapy",
        "tagline": "Stealthy viruses that teach your immune system to kill cancer.",
        "body": "**Tl;dr**: We are [Kopra Bio](https://www.linkedin.com/company/kopra-bio), a serial biotech entrepreneur and a UCSF Neurosurgery resident on a mission to revolutionize cancer treatment. Kopra Bio is curing cancer using viral immunotherapy technology we developed at UCSF. We’re making the next Keytruda, a **$25B/yr cancer drug blockbuster**, starting with the most aggressive form of brain cancer, glioblastoma. In preclinical models, we improve survival rates from 0% with standard-of-care treatment to **greater than 90% survival with our treatment.**\n\n**—**\n\nHey - we are Andrew and Alex!\n\n![uploaded image](/media/?type=post&id=83219&key=user_uploads/214420/8312b484-2aee-4ae8-bd7b-bc708051b7d1)\n\n# **Team:**\n\n🤵 [**Andrew Bartynski, PhD**](https://www.linkedin.com/in/andrewbartynski/) - Serial Biotech Entrepreneur and **repeat YC founder**. Developed the **contraceptive pill for men** as COO @ YourChoice Therapeutics (YC W19). Founded a **COVID-19 antiviral** company that went from founding to Phase II/III clinical trial in < 6 months and was acquired in < 1 year as COO @ ANA Therapeutics. Made **next-generation psychedelics** for mental health @ Alexander Shulgin Research Institute. Now curing cancer as CEO @ Kopra Bio.\n\n👨‍⚕️[**Alexander Haddad, MD**](https://www.linkedin.com/in/alexander-haddad-49b484305/) - Scientific Cofounder @ Kopra Bio, Neurosurgery Resident @ UCSF. Alex **published >** **70 peer-reviewed publications as a neurosurgery resident at UCSF**. He is particularly interested in **neuro-oncology** and the delivery of cell and **gene therapies** to the central nervous system. Alex helped spearhead the development of Kopra Bio’s viral technology in the labs of Dr. Manish Aghi and Dr. Noriyuki Kasahara. His clinical skills have also been recognized with the Krevans Award for clinical excellence at San Francisco General Hospital.\n\n👨‍⚕️ [**Manish Aghi, MD, PhD**](https://www.ucsfhealth.org/providers/dr-manish-aghi) - Neurosurgeon and Professor @ UCSF. Renowned expert with over **30 years of experience in Neurosurgery and tumor microenvironment** leading the Brain Tumor Research Center at UCSF with a focus on malignant brain tumors like glioblastoma.\n\n👨‍⚕️ [**Noriyuki Kasahara, MD, PhD**](https://braintumorcenter.ucsf.edu/people/noriyuki-kasahara) - CSO @ 4D Molecular Therapeutics, Professor of Neurological Surgery and Radiation Oncology @ UCSF. Dr. Kasahara has been a **world leader** in the field of **cancer research and gene therapy vector** **development** for over 30 years.\n\n**👩‍🔬** **Sara Collins, PhD** - Gene Therapy Specialist @ UCSF, Director @ 4D Molecular Therapeutics. World expert in the development of **vectors for cancer gene therapy**.\n\n# How do we get to a world without cancer?\n\n# **❌ Today’s approach** - death by a thousand cuts\n\nState-of-the-art cancer therapies target tumor mutations one by one. Despite more than 550 cancer drug approvals since the year 2000, there are still 10 million cancer deaths each year. This approach is expensive (**$1B per therapy**), slow (**10 years of development** per drug), and incremental (only **one mutation addressed** per therapy). Even the most successful cancer drugs of all time have **success rates of less than 10% in solid tumors**.\n\n# **✅ Kopra Bio’s Solution** - first principles approach that targets phenotypes seen across all cancers\n\nUsing **stealthy viruses🦠**, Kopra Bio infects cancer cells, spreads throughout tumors, and hijacks them to produce immune signals. The immune signals recruit T cells which then kill the cancer. This process teaches the patient’s immune system to kill their tumor - _in vivo_ - **curing the cancer and eliminating metastasis and recurrence.**\n\n![uploaded image](/media/?type=post&id=83219&key=user_uploads/214420/71da663f-8b6a-41da-81bf-179a4cddb385)\n\n# **🔬Kopra Bio at a glance**\n\n* **Demonstrated efficacy** in the most challenging brain cancer models increasing survival from 0% without treatment to **greater than 90% survival** with our treatment\n* **Cancer selective** by design - reduced toxicity and adverse events\n* **Platform** approach broadly applicable across cancer\n* Strong **safety** profile\n* Highly stable and **scalable** to manufacture\n\n# **🙏 Asks**\n\n* Share this post on your socials and help spread the word!\n* If you’re as excited as we are about eradicating cancer and would like to learn more about what we’re doing, **email us** at [founders@kopra.bio](mailto:founders@koprabio.com)",
        "url": "https://www.ycombinator.com/launches/LeF-kopra-bio-making-cancer-cures-using-viral-immunotherapy",
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        "created_at": "2024-08-20T15:08:44.470Z"
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    "id": 29733,
    "name": "1849 bio",
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    "long_description": "1849 bio designs microbes enabling cheap metal extraction allowing miners to unlock value from low quality copper and gold ores. Surprisingly, the mining industry is one of the largest scale users of biotech in the world with biomining processes accounting for ~1% of global copper production. Biomining is ultra-low cost, running around ~$1/ton of ore vs ~$7/ton for conventional processes. \r\n\r\nUnfortunately, while biomining is cheap, it can’t be applied to over 80% of copper ores, leaving vast resources without profitable extraction methods. An estimated ~$800B of copper sit today in waste materials and stockpiles with negative unit economics.\r\n\r\nWhile a great deal of effort has been spent on optimizing microbial metal extraction processes, very little effort has been spent on optimizing the microbes themselves. \r\n\r\nTo change that, we’re creating new biotech tools and platforms applied directly to the types of biology most relevant to miners. This enables us to develop new microbes and tackle some of the most difficult problems in biomining, unlocking billions in value from unprofitable resources while being more environmentally friendly than conventional processes.\r\n\r\nWe’re world class microbial engineers. We met while doing our PhDs in synthetic biology, where we spent our time applying and developing the most advanced bioengineering technologies to engineer living cells. ",
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    "team_size": 3,
    "industry": "Industrials",
    "subindustry": "Industrials",
    "launched_at": 1720558135,
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        "title": "1849 bio: Microbes for miners",
        "tagline": "Optimizing microbes for mining  🧬 ⛏️🪨",
        "body": "**TL;DR – We’re building microbes for metal extraction, enabling cleaner and cheaper mining of difficult-to-process ores. This will unlock 100M tonnes of copper stranded in ores that are not profitable to mine.** \n\n—\n\nHey all! We’re [Yongjin](https://www.linkedin.com/in/yongjin-park-6604092a/) and [Jai](https://linkedin.com/in/jai-padmakumar), co-founders of [1849 bio](https://www.1849.bio/), a team of genetic engineers developing microbes for metal extraction. We met while doing our PhDs at MIT and have spent the better portion of our adult lives hacking living organisms to do cool/useful tasks.  \n\n![uploaded image](/media/?type=post&id=82014&key=user_uploads/736094/55c73446-daaf-49c2-b6ec-cd654ab4a66e)\n\nNow, we’re on a mission to make the mining industry more sustainable and ensure a stable metal supply for the green energy transition.  \n\n### The Challenge:\n\nThe drive to electrify the world is driving an unprecedented demand for metals like copper, nickel, lithium, and other rare earths. Unfortunately, ramping up metal production is incredibly difficult. Copper is expected to face a \\~20% supply gap by 2031, threatening to slow the rollout of critical technologies like EVs and wind turbines.   \n\nNew mines are difficult to open, taking an average of 16.5 years in the US _after_ a new ore body has been identified. These bodies of metal themselves have become more difficult to identify. After decades of selective mining, **most copper today exists in low grade ores** (ore that contains very little copper), **making economical extraction with conventional methods impossible**.  \n\nApproximately 100M tonnes of copper is trapped in stockpiles and waste materials without any means to profitably extract the metal.  \n\n### The Solution:\n\nBiology! It turns out the mining industry is one of the largest-scale users of biotech in the world. \\~1.2% of copper production in 2019-2020 was done using microbes in a process called “bioleaching.” The basic idea here is to: \n\n1. Stack ore into a giant pile — think miles long and 100s of feet tall. This pile is called a “heap.” \n\n   ![uploaded image](/media/?type=post&id=82014&key=user_uploads/736094/146fb0a7-bff6-421f-81e9-41eea8e38d68)\n\n2. Grow a very unique group of organisms that love acid and use both iron in the rocks and CO2 from the air as food sources. This triggers a series of chemical reactions that ultimately separates the solid copper from the ore into a liquid form.\n3. Collect this liquid at the bottom and run a current through it.\n\nThen you have pure copper! \n\nMiners have been using this process for copper extraction for decades (and in some cases, for other metals like gold, nickel, and cobalt). The process is cleaner and \\~7x cheaper than conventional processes. It also produces fewer tailings (mine waste), a growing environmental problem and liability for miners \n\nWhy isn’t this used everywhere? \n\n1. **It doesn’t work for 80% of copper ores**. Owing to some complex chemistry, the dominant copper ore, chalcopyrite, can’t be processed using bioleaching.  \n2. **It’s really slow, narrowing its use cases**. While a conventional process can melt ore down and give you pure copper in an hour, bioleaching operates on a timescale of months to years.\n\nWe’re using our deep background in bioengineering to build platforms and tools specifically for the types of biology miners need. By engineering the microbes themselves, we are introducing a new set of capabilities to the mining industry: \n\n* **Bioleaching solutions that work on difficult-to-process ores**, like chalcopyrite, unlock billions of dollars of value currently trapped in uneconomical materials. \n* **Faster metal extraction timelines**, getting miners their cash faster. \n* **Low CapEx, sustainable metal extraction**. Our solutions fit into existing mine infrastructure and processes.\n\n# Our asks:\n\n1. If you know anyone in the mining industry, we’d love to meet them! \n2. If you know anyone working in the e-waste recycling industry, we’d love to chat with them, too. We can tune our process for e-waste recycling and are looking into applications for that as well.\n\nReach out to [intro@1849.bio](mailto:intro@1849.bio)!",
        "url": "https://www.ycombinator.com/launches/LKo-1849-bio-microbes-for-miners",
        "votes": 28,
        "created_at": "2024-07-17T04:20:31.752Z"
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    "id": 29749,
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    "all_locations": "Philadelphia, PA, USA",
    "long_description": "Finding new cures requires seeing what and where the abnormal molecules are in a diseased tissue, but current tools see less than 1% of those molecules. Biocartesian combines microscopy and new chemistries to see 50X more, offering unprecedented insights into disease biology and new therapies.",
    "one_liner": "Converting biology into digital molecular maps",
    "team_size": 2,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Drug Discovery and Delivery",
    "launched_at": 1724915415,
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        "full_name": "Ian Dardani",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder of Biocartesian. Microscopy + molecular tools + engineering. Previously Bioscience Enterprise at Cambridge, Bioengineering PhD at UPenn.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/ian-dardani",
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      {
        "user_id": 1975436,
        "full_name": "Jess Li",
        "title": "Founder",
        "founder_bio": "Co-founder of Biocartesian. Genetics + epigenetics, next-gen sequencing. Previously PhD at UPenn, BD at imvaria.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/jessxli/",
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      {
        "id": 83575,
        "title": "Biocartesian - Converting biology into digital molecular maps",
        "tagline": "We use chemistry and microscopy to help scientists discover new drugs.",
        "body": "**Tl;dr**: Finding new cures requires knowing **what** and **where** the abnormal molecules are in a diseased tissue, but current tools see less than 1% of those molecules. [Biocartesian](https://www.biocartesian.com/) combines microscopy and new chemistries to see a comprehensive picture, offering unprecedented insights into disease biology and new therapeutic approaches.\n\n![uploaded image](/media/?type=post&id=83575&key=user_uploads/89898/ee18ecba-242d-4cb0-bb1b-d9ab6de69ddd)\n\n# **👩‍🔬 Team**\n\nWe’re [**Ian**](https://www.linkedin.com/in/ian-dardani) and [**Jess**](https://www.linkedin.com/in/jessxli/), two former UPenn PhDs on a mission to see every molecule in biological specimens. We’ve spent years advancing and troubleshooting the latest tools in biology, from DNA sequencing to microscopy, but even with these efforts, these tools revealed only a fraction of the underlying biology. We dreamed of a way for scientists to see everything (and fast) so they could reduce their guesswork and focus on the most promising therapeutic approaches.\n\n# ❌ **Problem**\n\nMost diseases remain a mystery because existing methods can see less than 1% of the molecules within a biological specimen. These methods can also take months for scientists to set up and often fail, wasting thousands of dollars per specimen and millions in failed drug programs.\n\n# 🔬 **Our approach**\n\nWe’re combining new chemistries and microscopy to see a full map of the molecules in biological specimens. Imagine if we could see into the \\~500 million existing tumor specimens to decode these cancers.\n\nTo use our technology, scientists simply ship their biological specimens to our laboratory, where we automate their conversion into digital molecular maps – saving them months of tedious data collection and troubleshooting.\n\n![uploaded image](/media/?type=post&id=83575&key=user_uploads/1116949/7deca4d9-cf29-45b4-852d-1fc471672b75)\n\nOur solution:\n\n* Enables scientists in any disease area to 50X their data collection\n* Saves scientists months of time\n* Lowers the cost of biological measurements\n* Expands access to bespoke assays developed by any scientist by integrating with our platform\n* Meets the growing demand for biological data as AI-led analysis supersedes the bottleneck of human-led analysis\n* Turns biology from 99% invisible to an interpretable problem\n\n# **🙏 Asks**\n\nIf you’re excited about this and would like to learn more, email us at:\n\n[**interested@biocartesian.com**](mailto:interested@biocartesian.com)\n\nor reach us at <https://www.biocartesian.com>",
        "url": "https://www.ycombinator.com/launches/Ljz-biocartesian-converting-biology-into-digital-molecular-maps",
        "votes": 265,
        "created_at": "2024-08-29T15:17:26.070Z"
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    "id": 29794,
    "name": "Baseline AI",
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    "website": "https://www.baselinetrials.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "The main document in a clinical trial is the study protocol. At large companies, floors of people use the protocol to create the study data collection forms, clinical database design, error checks, analysis code, and data transformation mappings. We use AI to automate the process of creating everything from the protocol, saving not only spend on headcount but also months of time which can translate to up to $27M in direct costs + lost revenue saved for a single phase 3 trial. ",
    "one_liner": "AI document creation and data management for clinical trials",
    "team_size": 2,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Healthcare IT",
    "launched_at": 1724199477,
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      "Health Tech",
      "B2B",
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2024",
    "status": "Inactive",
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      "Healthcare IT"
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      "America / Canada"
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    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/baseline-ai.json",
    "year_founded": 2023,
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    "country": "US",
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        "full_name": "Zoe Sheill",
        "title": "Founder",
        "founder_bio": "Co-founder of Baseline AI. Previously MIT, SpaceX. Ask me about the Banana Lounge at MIT",
        "is_active": false,
        "linkedin_url": "https://linkedin.com/in/zoesheill",
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      {
        "user_id": 718972,
        "full_name": "Nabil Baugher",
        "title": "Founder",
        "founder_bio": "Baseline AI Co-founder. Previously ML at YouTube and software at Slack/Audible. CS + Neuro at MIT.",
        "is_active": false,
        "linkedin_url": "https://linkedin.com/in/nabil-baugher-93822a18a",
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      {
        "id": 83484,
        "title": "Baseline AI: AI Document Creation + Data Management for Clinical Trials",
        "tagline": "Getting treatments out of the lab and into the hands of patients that need them",
        "body": "### **Tl;dr**\n\n[Baseline’s](https://www.baselinetrials.com/) AI platform helps pharmaceutical companies eliminate months of manual work during clinical trials by automating the study build, data transformation, and analysis creation. This can save a single trial up to $18M in direct costs and lost revenue.\n\n### **⚠️ Problem**\n\nThe main document in a clinical trial is the study protocol. At large companies, floors of people use the protocol to create the study data collection forms, clinical database design, error checks, analysis code, and data transformation mappings. This makes the process both expensive and time-consuming, with **a single day lost** in a clinical trial **costing** [**50k-300k** (depending on phase)](https://baselineai.notion.site/Cost-of-1-Day-By-Stage-af2f3e4008444ed8a819d288d45fd31d?pvs=74). \n\n### **🚀 Solution**\n\nWe offer a three-part solution:\n\n* **Baseline Build**\n  * Processes the study protocol to create the study data collection forms, clinical database design, and error checks\n* **Baseline Harmonize**\n  * Transforms data from external sources into the desired format based on the database model\n* **Baseline Analyze**\n  * Uses the study protocol and database model to create the analysis tables and figures\n\nImplementing our AI workflows can **save a single phase 3 clinical trial up to $18M** in direct costs and lost revenue.\n\n![uploaded image](/media/?type=post&id=83484&key=user_uploads/718972/f7dbfa5f-f8d8-436f-9ee5-29139b3f9de7)\n\n**📘 Backstory**\n\nWe’re [Nabil](https://linkedin.com/in/nabil-baugher-93822a18a) and [Zoe](https://linkedin.com/in/zoesheill) — two recent MIT grads who want to help bring new treatments to patients in need faster. We noticed that despite drug discovery being faster than ever, new treatments for things our friends and family needed seemed to rarely make it to market. Zoe also participated in a clinical trial herself and wanted to see the system improve. \n\n### **🙏 Ask**\n\nIf you or anyone you know are in pharma/biotech or running clinical trials, we’d love to chat! Please reach out to us at [founders@baselinetrials.com](mailto:founders@baselinetrials.com).",
        "url": "https://www.ycombinator.com/launches/LiW-baseline-ai-ai-document-creation-data-management-for-clinical-trials",
        "votes": 98,
        "created_at": "2024-08-27T21:22:07.159Z"
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    "id": 29986,
    "name": "Exin Therapeutics",
    "slug": "exin-therapeutics",
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    "website": "https://www.exintherapeutics.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We use AI and high-throughput mouse studies to discover therapeutics that modify neural activity in the brain. The initial focus is on epilepsies associated with autism and Parkinson’s disease with the potential to expand to any neurological disorder underlined by a dysfunction in neural activity. Our R&D is guided by AI models trained on high-density multimodal mouse data generated in-house.\r\n\r\nWe are 3 Oxford-trained neuroscientists supported by an experienced SAB from Harvard, Science Corp (ex-Neuralink), EPFL, Meta, and UCL.\r\n\r\nIn 1.5 months since landing in SF, we went from nothing to (1) opening and operating an animal lab in South SF, (2) obtaining a proof-of-concept in mice, (3) building a multimodal AI model that directs our screening approach and (4) submitting two provisional patents; all while utilizing less than 50% of our YC funding. \r\n",
    "one_liner": "AI drug discovery platform for neurotherapeutics",
    "team_size": 9,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Therapeutics",
    "launched_at": 1736839155,
    "tags": [
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      "Biotech",
      "Therapeutics"
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2025",
    "status": "Active",
    "industries": [
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    "regions": [
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      "America / Canada"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/exin-therapeutics",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/exin-therapeutics.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/exin-therapeutics",
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      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
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      {
        "user_id": 1960104,
        "full_name": "Gabriel Ocana Santero",
        "title": "Founder/CEO",
        "founder_bio": "Neuroscientist with a background in biochemistry and research on gene therapy, pancreatic cancer, and muscle physiology at institutions in Boston, Vienna, and Stockholm. Earned MSc in Neuroscience and PhD in Pharmacology at Oxford Uni, focusing on neurodevelopment and the effects of SSRI anti-depressants on early patterns of neural activity. Used neuroAI models at the Gatsby Unit (UCL) to study serotonin’s role in cognition. Currently developing gene therapies for neurological conditions. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/gabriel-ocana-santero-85b41819a",
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        "full_name": "Marko Tvrdic",
        "title": "Founder/COO",
        "founder_bio": "Co-founder at Exin Therapeutics.\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/marko-tvrdic/",
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      {
        "id": 86986,
        "title": "Exin Therapeutics: An AI discovery platform for neurotherapeutics",
        "tagline": "Precise modulation of neural activity across neurological disorders.",
        "body": "[Exin Therapeutics](https://www.exintherapeutics.com/) uses AI and high-throughput mouse studies to develop therapies for people suffering from neurological and neuropsychiatric disorders. We are tackling conditions like epilepsy, debilitating symptoms of autism spectrum disorder (ASD), and Parkinson’s disease by focusing on the neural circuitry of the brain.\n\n# **Why is this important?**\n\nMillions of people around the world suffer from neurological disorders. These disorders arise from imbalances in brain activity. Think of it like an orchestra where some instruments are playing too loud while others are too quiet. This “bad music” / neural imbalance—what we call excitatory/inhibitory (E/I) imbalance—is the root cause of many symptoms in these disorders. For example, in epilepsy, overactive circuits lead to seizures, while in ASD, it can cause sensory hypersensitivities - where individuals feel overwhelmed by normal sounds, lights, or touch that most people wouldn't even notice. These dysregulations in neural circuits impact how the brain processes information and responds to the world, leading to the debilitating symptoms we see in these disorders.\n\n# **Our solution**\n\nWe’re taking a circuit-level approach to treating brain disorders. Instead of trying to restore missing genes, we’re supplementing already existing genes that we know will be directly changing the neural activity in the brain - in the direction that we want and independently of the genetic background.\n\n![uploaded image](/media/?type=post&id=86986&key=user_uploads/89898/4181e602-6279-4611-9af3-73b0ccfca768)\n\nWe are also working on a multimodal AI model to streamline our preclinical R&D. Why? Well, because developing drugs for the brain is really tough. Every candidate needs to be tested for its ability to modulate circuits, and doing that through a traditional drug development preclinical pathway is a slow process.\\\n\\\nBecause of that, we use ML to analyze high-density multimodal data sets and prioritize which drugs are worth testing further. We use neural activity, behavior, and transcriptomics to train multimodal AI models that identify the drivers of the disease not observable by conventional analytical methods.\n\n![uploaded image](/media/?type=post&id=86986&key=user_uploads/89898/b1ad1c24-a513-4633-83f6-58378d1cb5d8)\n\nThen, we use these models to (1) predict the potential of our therapies to treat particular indications and (2) provide an efficacy readout during our internal experimental testing. This way, we spend our time (and funding) testing the most promising candidates.\n\n# **Our ask**\n\n1. Intros: If you have contacts in pharma, working on complementary tech, or have ideas for partnerships, we’d love to connect.\n2. We'd love to hear from people who have worked in therapeutics and people with IND-enabling programs or clinical trial design experience.\n\n# **Team**\n\nWe are a team of 3 Oxford-educated neuroscientists. [Gabriel](https://www.linkedin.com/in/gabriel-ocana-santero-85b41819a/?originalSubdomain=uk) is a biochemist with a wealth of research experience in neural development, gene therapy, and NeuroAI. He holds a PhD in Pharmacology from Oxford. [Ivan](https://www.linkedin.com/in/ilazarte/) is a physicist by training and has worked in molecular biology, circuit, and systems neuroscience. Ivan’s brother suffers from ASD and Ivan is on a mission to improve the quality of life for patients suffering from debilitating neurological conditions. He holds a PhD in neuroscience from Oxford. [Marko](https://www.linkedin.com/in/marko-tvrdic/?originalSubdomain=uk) has experience with iPSC models, retinal disorders, and circadian and developmental neuroscience. During his PhD, he worked in systems and behavioral neuroscience and holds an MSc from Oxford.\n\n![uploaded image](/media/?type=post&id=86986&key=user_uploads/1960211/5487d1f0-6cbc-4728-af74-a8377516e2d3)\n\nWe are supported by a team of advisors from top industry and academic institutions - including Harvard & UCL.",
        "url": "https://www.ycombinator.com/launches/Md0-exin-therapeutics-an-ai-discovery-platform-for-neurotherapeutics",
        "votes": 41,
        "created_at": "2025-01-20T22:29:09.667Z"
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    "id": 29988,
    "name": "Delineate",
    "slug": "delineate",
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    "website": "https://delineate.pro/",
    "all_locations": "Boston, MA, USA; Remote",
    "long_description": "Delineate is building AI agents to design better clinical trials faster.\r\n\r\nThis is a $10B dollar market, where saving even 1 day along the process is equal to $1-5M in revenue potential.\r\n\r\nWe process existing clinical trial research into specialized datasets saving scientists months of effort.\r\n\r\nDelineate is working with two of the largest pharmaceutical companies. With one of our customers, we are helping them get to the next phase of trials faster by creating the largest dataset on a drug class ever constructed.",
    "one_liner": "Agents for Accelerated Clinical Trial Design",
    "team_size": 20,
    "industry": "B2B",
    "subindustry": "B2B -> Engineering, Product and Design",
    "launched_at": 1739310156,
    "tags": [
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      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
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    "founders": [
      {
        "user_id": 1994202,
        "full_name": "Emily Nieves",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder at Delineate. Previously she was an MIT PhD Candidate in Biological Engineering and worked at Pfizer and AstraZeneca where she focused on applying AI to modeling and simulation for drug development optimization. ",
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        "linkedin_url": "https://www.linkedin.com/in/emily-nieves-a67b90144/",
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      {
        "user_id": 1991260,
        "full_name": "Jawad Iqbal",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder and CTO of Delineate. Masters thesis in Robotics/AI with 2 research publications. Former technical project manager for high impact digital transformation projects within Skunkworks (Advanced Development) division at Lockheed Martin. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/jawad-iqbal/",
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      {
        "id": 87668,
        "title": "Delineate - Information Overload to Insights in Biopharma",
        "tagline": "Extract meaningful data from biopharma research and patents at scale",
        "body": "![uploaded image](/media/?type=post&id=87668&key=user_uploads/1994202/81ed81f4-b8e2-4a07-b7c7-70d09ac5b497)\n\n### **TLDR**\n\n* Delineate extracts and analyzes data from biopharma research papers and patents at scale, including data trapped in figures and plots.\n* We are partnered with two top-10 pharma companies where we are performing some of the largest systematic-review studies ever conducted.\n* For our clients we have increased the number of processed studies by 15x compared to industry standard and reduced the time for certain studies from months to weeks.\n\n### **Problem**\n\n90% of drug candidates fail despite billions in pharmaceutical R&D. Making evidence based decisions is crucial to the success of $2B drug pipelines. However to aggregate evidence across hundreds of research papers and patents can take research scientists months of hard labor.\n\n### **Solution**\n\nDelineate has developed custom LLM and computer vision models paired with rigorous QC processes to deliver extremely high quality datasets in a fraction of the time. By increasing the scale and speed for evidence aggregation, Delineate is transforming how pharmaceutical companies make evidence based development decisions.\n\n### **Demo:**\n\n[**Here is a quick demo of our platform and a sample project:**](https://www.loom.com/share/66a1878825f14eabaeee7f5cf948c9a1?sid=55675f9b-2d71-40d2-afc3-4be5351fc80f)\n\n### **Ask:**\n\n* Any introductions to biopharma companies that perform meta-analyses and/or computational analysis workflows.\n* If any biopharma-focused YC companies need assistance, we're happy to help!\n\n# About Us:\n\nled by [**Emily Nieves**](https://www.linkedin.com/in/emily-nieves-a67b90144/) (MIT PhD, former Pfizer/AstraZeneca computational researcher) and [**Jawad Iqbal**](https://www.linkedin.com/in/jawad-iqbal/) (ex-Lockheed Skunkworks AI lead)\n\n![uploaded image](/media/?type=post&id=87668&key=user_uploads/1994202/20a27c7b-6a42-4af4-953a-2a6d9883c8a8)\n\n",
        "url": "https://www.ycombinator.com/launches/Mo0-delineate-information-overload-to-insights-in-biopharma",
        "votes": 14,
        "created_at": "2025-02-13T20:40:24.989Z"
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    "id": 30039,
    "name": "Raycaster",
    "slug": "raycaster",
    "former_names": [
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    "website": "https://eval.raycaster.ai",
    "all_locations": "New York City, NY, USA",
    "long_description": "Raycaster began as an agentic AI platform for knowledge work, serving dozens of publicly traded companies across life sciences, medical devices, energy, and oil and gas. It has since launched a division dedicated to training and evaluating AI systems in these industries, working directly with leading AI labs and enterprises to improve their ability to perform economically valuable work.",
    "one_liner": "Agentic AI for knowledge work",
    "team_size": 6,
    "industry": "Healthcare",
    "subindustry": "Healthcare",
    "launched_at": 1729820914,
    "tags": [
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      "Data Labeling",
      "Enterprise Software",
      "AI"
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    "batch": "Fall 2024",
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    "industries": [
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      "America / Canada",
      "Remote",
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    "url": "https://www.ycombinator.com/companies/raycaster",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/raycaster.json",
    "year_founded": 2025,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/raycasterai",
    "x_url": "https://x.com/raycasterai",
    "fb_url": "https://www.facebook.com/raycasterai",
    "cb_url": "https://www.crunchbase.com/organization/raycaster",
    "github_url": "https://huggingface.co/datasets/raycasterai/",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8e3854e63389e5cabd44e4b6563fefc3bd539147.png",
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    "primary_partner": {
      "name": "Diana Hu",
      "url": "https://www.ycombinator.com/people/diana-hu"
    },
    "founders": [
      {
        "user_id": 299659,
        "full_name": "Levi Lian",
        "title": "Founder/CEO",
        "founder_bio": "Raised in a four-generation medical family, Levi chose computer science to accelerate research rather than practice it. At Stanford he worked in AI labs on early foundation model applications, then shipped enterprise-ready AI at Solvvy (acquired by Zoom), Intuit and Uber AI. Levi combines deep life-science empathy with proven AI product execution to compress drug-development timelines.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/levi-lian",
        "x_url": "https://twitter.com/levilian1",
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    "jobs": [
      {
        "id": 81306,
        "title": "CMC Workflow Specialist (Analytical/Tech Transfer)",
        "url": "https://www.ycombinator.com/companies/raycaster/jobs/C4HEspJ-cmc-workflow-specialist-analytical-tech-transfer",
        "location": "New York, NY, US / Remote",
        "type": "Contract",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$40 - $130 / hourly",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizenship/visa not required",
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      },
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        "id": 81307,
        "title": "Clinical Trial Enablement Workflow Expert",
        "url": "https://www.ycombinator.com/companies/raycaster/jobs/ANItObm-clinical-trial-enablement-workflow-expert",
        "location": "New York, NY, US / Remote",
        "type": "Contract",
        "role": "Operations",
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        "salary_range": "$40 - $130 / hourly",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizenship/visa not required",
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    "news": [
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        "title": "Levi Lian, CEO of Raycaster, on why vertical AI is workflows first & chat last | Sacra",
        "url": "https://sacra.com/research/levi-lian-raycaster-vertical-ai-workflows/",
        "date": "Sep 30, 2025"
      },
      {
        "title": "Raycaster funding & analysis | Sacra",
        "url": "https://sacra.com/c/raycaster/",
        "date": "Sep 30, 2025"
      },
      {
        "title": "Raycaster: AgentX winner",
        "url": "https://rdi.berkeley.edu/agentx/",
        "date": "Jul 21, 2025"
      },
      {
        "title": "Agentic AI Summit Main Stage Speaker",
        "url": "https://rdi.berkeley.edu/events/agentic-ai-summit",
        "date": "Jul 09, 2025"
      },
      {
        "title": "Founder Fellowship 2025 Participants | NYCEDC",
        "url": "https://edc.nyc/founder-fellowship-2025-participants",
        "date": "Mar 06, 2025"
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    "launches": [
      {
        "id": 116908,
        "title": "Biopharma Bench 0.1 – Evaluating AI on Autonomous Drug Development",
        "tagline": "Frontier agents struggle in drug development; open models crush on cost",
        "body": "**TL;DR:** Everyone is trying to use AI for drug _discovery_ (finding molecules), but 90% of actual pharma time and money gets burned in drug _development_ - people digging through messy SharePoint, Veeva, fixing conflicting batch records (often scanned), updating clinical protocols, and answering FDA audit letters with 20 redline versions and siloed email threads with colleagues.\n\n![uploaded image](/media/?type=post&id=116908&key=user_uploads/299659/82cec064-a13e-4bfc-b527-31b46388e50c)\n\nWe built **Biopharma Bench 0.1**: 12 complete, reconstructed drug companies, 71 multi-hour tasks, and 752 ground-truth checks audited by industry veterans to see what happens when you actually put frontier AI into an employee’s seat.\n\n> One interesting finding: DeepSeek V4.1 Flash matched GPT-6 Sol on the actual work (57.0% vs 54.3%) at a sixth of the cost ($0.11 vs $0.64 per task). Meanwhile Claude Opus 5, #2 overall at 63.8%, didn't fully pass a single task.\n\nCheck out the interactive traces &amp; leaderboard: <https://eval.raycaster.ai/benchmarks/biopharma-bench>\\\nRead the deep-dive blog post: <https://www.raycaster.ai/blog/biopharma-bench>\\\nJoin the discussion on Hacker News: <https://news.ycombinator.com/item?id=49845891>\\\nX: <https://x.com/dubrovnik07/status/2103543413933064530>\\\nLinkedIn: <https://lnkd.in/p/g_PD7jYr>\n\n### Backstory\n\nHey everyone! We’re the team behind Raycaster (<https://raycaster.ai>).\n\nWe first started building AI agents for regulated industries like biopharma because we see the inevitable wave of knowledge work getting handled by AI systems with human oversight (definitely not fear-mongering, but in a super exciting way!).\\\n\\\nOver the last few months, our major focus has been helping frontier AI labs globally hillclimb capabilities in underserved industries such as biopharma, energy, data centers, among others.\n\nVery quickly, we noticed something broken: almost all bio AI benchmarks test multiple-choice questions or molecular physics. Nobody was testing whether an AI agent could actually do the drug development work, open a folder of 50 disorganized PDFs and spreadsheets, find where two lab reports contradict each other, and draft an FDA-defensible document.\n\nSo, we spent the last few months working directly with former pharma directors and regulatory specialists to build an evaluation that tests what real work actually looks like.\n\n---\n\n### The Problem: The \"Fluency Trap\"\n\nIt takes 8–10 years and over $1B to get a drug to patients. Most of that delay is paperwork and coordination mistakes:\n\n* Protocol Drift: \\~75% of clinical trials have to amend their protocols mid-flight. Each amendment costs hundreds of thousands of dollars and months of lost time.\n* Messy Records: A tiny discrepancy between a stability test memo from last Tuesday and an executed batch record can trigger FDA warning letters (Form 483s) or clinical holds.\n\nWhen people play with LLMs for this, they fall into what we call the fluency trap: Frontier models write seemingly true prose. If you just read the output, the AI sounds like a 20-year VP of Regulatory Affairs who knows everything.\n\nBut when you actually grade the facts against ground truth, the model cited an outdated 2023 draft instead of the approved amendment, or hallucinated a purity threshold. In biopharma, being 95% right with one made-up number is an instant rejection.\n\n![uploaded image](/media/?type=post&id=116908&key=user_uploads/299659/46c7d62a-3842-4e51-8680-ff7f80f37255)\n\n### How Biopharma Bench Works\n\nWe didn’t give the models clean, cherry-picked prompts. We dropped them into messy environments:\n\n* 12 Companies: Real reconstructed company drives packed with thousands of messy files and software states - unredacted study reports, supplier quotes, assay validations, and random internal memos.\n* 71 Long-Horizon Tasks: Multi-hour workflows across Preclinical, CMC/Manufacturing, Clinical Ops, and Regulatory Affairs (like investigating an out-of-spec stability result or drafting a protocol amendment).\n* 752 Ground-Truth Checks: Graded against falsifiable rubrics created using real documents along with former pharma directors.\n\n![uploaded image](/media/?type=post&id=116908&key=user_uploads/299659/36d4376d-59ed-4eba-9ede-d93930299e98)\n\n---\n\n### What Shocked Us from the Runs\n\nWe ran frontier reasoning models across multiple popular harnesses (Codex, Pi, Claude Code). Here are the 3 biggest takeaways:\n\n1. **DeepSeek matched GPT-6 Sol at 1/6 the cost.** \n2. **Almost nobody finishes a full task cleanly.** Top systems get 45–70% of checks, but full passes are rare: GPT-6 Astra 8/71, Grok 4.6 2/71, Kimi K3 and GPT-5.6 Sol 1 each. Claude Opus 5, DeepSeek, GPT-6 Sol, GLM, and Gemini got 0.\n3. **The writing is fine; the sourcing isn't.** In one public task, Claude Opus 5 did everything right on the surface: it navigated the folders, read scanned protocols page by page, and wrote a clean brief. But a secondary report claimed a \"protocol-defined 30-minute window\" that doesn't exist in the controlling protocol. Opus repeated it as fact in the brief for the inspector. An experienced engineer would have flagged it.\n\n---\n\n### See the Traces\n\n![uploaded image](/media/?type=post&id=116908&key=user_uploads/299659/10059154-ce98-41cb-b0ed-f23ac8942580)\n\n---\n\n### How We Can Help\n\n* Naturally as a data / research lab, we have tons of insights into benchmark design - we recently became a proud sponsor of [evalscience.org](https://evalscience.org), a 501(c)(3) nonprofit focused on scientific agent evaluation methods and independent benchmark verification. If there’s interest, happy to share more regularly on BookFace\n* We work with / fund academics and researchers and have landed joint papers in NeurIPS, NAACL and all the big conferences. Bring your idea to us and see what can come out of it\n* Finally, if you’re a neo-lab reading this, we have lots of ready to license data, environments and evals on some of the most interesting yet underserved industries 😄 , talk to us at [team@raycaster.ai](mailto:team@raycaster.ai)",
        "url": "https://www.ycombinator.com/launches/UPc-biopharma-bench-0-1-evaluating-ai-on-autonomous-drug-development",
        "votes": 15,
        "created_at": "2026-09-25T17:16:44.511Z"
      },
      {
        "id": 95955,
        "title": "Raycaster - Cursor for regulatory documents in drug development",
        "tagline": "AI workspace for high-stakes documents, not just code.",
        "body": "Hey YC 👋 I’m Levi, founder of Raycaster.\n\n### TL;DR\n\n* Raycaster is **Devin/Cursor, but for life sciences documents instead of code.**\n* It does **project-level drafting** across hundreds of PDFs, Word, Excel, and PowerPoints - not just “write a file from scratch.”\n* We’re starting with **drug development docs** (clinical, CMC, regulatory, quality) where delays and mistakes are insanely expensive.\n\n**Video (1 min):** [link](https://youtu.be/FhpC9dyBUF4)\n\n---\n\n### The problem (in plain English)\n\nIn drug development, everything important lives in documents:\n\n* protocols, reports, validation plans\n* batch records, specs, methods\n* Module 3, labels, responses to regulators\n\nThey’re scattered across SharePoint, Veeva, email, vendor portals, etc. When something changes - dose, batch size, impurity limit - you’re supposed to update _dozens_ of dependent docs so nothing contradicts anything else.\n\nToday that means:\n\n* **Research:** hunting across PDFs, Word, Excel, PowerPoints and public guidances for the right paragraph or table\n* **Draft:** copy-pasting into new docs and hand-editing boilerplate that “mostly” matches\n* **Edit:** reviewers tracking changes by eye and leaving 200 comments per document\n* **Version control:** folders called `FINAL_v7_REALLY_FINAL_clean(2).docx` and hoping nobody sends the wrong one to the agency\n\nThis happens across _every_ stage of drug development - nonclinical → clinical → CMC → labeling. When documents drift, you get delay letters, extra review cycles, or a straight-up rejection. It’s one of the big reasons timelines slip.\n\nChatGPT / Copilot can draft a single file. They don’t understand a **cross-document project** with real regulatory consequences.\n\n---\n\n### What we built\n\nRaycaster is an **AI workspace for document projects**, starting with regulatory and CMC work.\n\nThink of a repo, but for your dossiers:\n\n* A **workspace** that holds your source docs (guidances, prior submissions, lab reports, CoAs, batch records…).\n* A graph of how they depend on each other (this spec feeds this batch record feeds this Module 3 section, etc.).\n* An AI agent that can **research, draft, edit, and reconcile** across all of them—with citations and review controls.\n\nConcretely:\n\n1. **Research, with real citations**\\\n   Ask: “What does EMA say about a 5× batch size increase?” or “Which CoAs support these new commercial lots?”\\\n   Raycaster reads mixed formats (PDF, DOCX, XLSX, PPTX), pulls out the relevant paragraphs/tables, and shows you the answer with pinned citations back to the exact page and cell.\n\n   ![uploaded image](/media/?type=post&id=95955&key=user_uploads/299659/18f9f9a3-3ea8-49c7-b50f-17df326e526e)\n\n2. **Project-level drafting**\\\n   Instead of “write me a cover letter,” you say “update the batch formula + cover letter + justification for the 5× scale-up.”\\\n   Raycaster drafts the actual Word docs in your workspace, using the research + your templates, and wires in all the cross-references.\n\n   ![uploaded image](/media/?type=post&id=95955&key=user_uploads/299659/a80d77f1-fbde-429f-b80d-30153df19a1e)\n\n3. **Structured editing**\\\n   Editing is usually harder than drafting: “bump the batch size everywhere, but don’t touch the control batch,” “update only the EMA version,” etc.\\\n   Raycaster does **diff-aware edits**: it proposes precise changes inline, shows red/green diffs, and explains _why_ each change was made.\n\n   ![uploaded image](/media/?type=post&id=95955&key=user_uploads/299659/4b1ab418-fbe1-4517-a157-f54b076a9ca5)\n\n4. **Version control + remote agents**\\\n   Every change is tracked like a commit. You see: who asked for it, what files were touched, what the diff was, and what sources were used.\\\n   You can let Raycaster run “in the background” on a workflow (e.g. keep Module 3 in sync with new CoAs), then come in as a human reviewer to approve or tweak before anything is finalized.\n\nAll of this lives in a collaborative, access-controlled workspace, so legal/reg affairs/IT don’t freak out.\n\n---\n\n### Why this is technically interesting\n\nFor devs: this is all the fun of agents and tooling, but on **documents instead of code**:\n\n* Retrieval over **huge, messy, mixed-format corpora** (PDF/DOCX/XLSX/PPTX, scanned tables)\n* A maintained **dependency graph** of citations and document links, so the agent can do impact analysis instead of one-off Q&A\n* A diff engine that understands “regulatory text” and tables (git diff , not just lines of code\n* Multi-agent workflows with strict evaluation + human checkpoints, because “just let the model commit” is not an option when the FDA is on the other side\n\n---\n\n### Why we’re doing this\n\nI started Raycaster after seeing the same pattern over and over: the science was fine; **the documents were the bottleneck.** Teams were shipping “AI pilots,” but the real work was still people copying text between PDFs and Word.\n\nWe think documents deserve the same treatment code got:\\\n**good tooling, real version control, and smart agents that understand context.**\n\n---\n\n### Ask\n\nWe’re opening prioritized access for:\n\n* **Biopharma, med-tech, and device companies** with heavy document work (regulatory, CMC, clinical, quality, safety) on major submissions or post-approval changes.\n* **Engineers / researchers** who care about serious agentic systems on messy document workflows and want to kick the tires or collaborate on evals/benchmarks.\n\nIf that’s you (or someone you know):\n\n👉 Email [**founders@raycaster.ai**](mailto:levi@raycaster.ai) with the subject **“YC Pilot”** and 2-3 sentences about your team + doc stack, or just ping me at [**raycaster.ai**](http://raycaster.ai).\n\n![uploaded image](/media/?type=post&id=95955&key=user_uploads/299659/474ffc8a-305f-4986-ba5e-b236cf06dd90)\n\n",
        "url": "https://www.ycombinator.com/launches/Oxf-raycaster-cursor-for-regulatory-documents-in-drug-development",
        "votes": 19,
        "created_at": "2025-12-03T18:56:35.429Z"
      }
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    "id": 30049,
    "name": "Reticular",
    "slug": "reticular",
    "former_names": [
      "Advocate"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/077de5cdaa18c1c7dd719188ef7fdeadce5020aa.png",
    "website": "https://reticular.com",
    "all_locations": "Boston, MA, USA",
    "long_description": "Reticular is scaling polygenic prediction for embryo selection, helping IVF couples plan their families today while building a platform to unlock cures for complex heritable diseases long-term.\r\n\r\nJohn competed Biology Olympiads before spending 4 years at MIT publishing ML/bio research in NeurIPS and Nature. We believe biological models encode far more information than anyone is currently using - our goal is to unlock this potential.",
    "one_liner": "Scaling Genomic AI for Embryo Selection",
    "team_size": 2,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Consumer Health and Wellness",
    "launched_at": 1731023720,
    "tags": [
      "Generative AI",
      "Consumer Health Services",
      "Biotech"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2024",
    "status": "Active",
    "industries": [
      "Healthcare",
      "Consumer Health and Wellness"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/reticular",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/reticular.json",
    "year_founded": 2024,
    "city": "Boston",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/reticular-yc-f24/",
    "x_url": "https://x.com/ReticularAI",
    "fb_url": null,
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        "body": "Hey everyone! We're [Nithin](https://www.linkedin.com/in/nithin-parsan) and [John](https://www.linkedin.com/in/johnyang101), founders of Reticular.\n\n**TL;DR:**\n\nReticular gives pharma companies precise control over protein AI models, enabling reliable drug discovery without millions in wasted experiments. We're unlocking encoded information in these models through foundational AI interpretability research, making steering as easy as prompting ChatGPT.\n\n**The Team:**\n\n![uploaded image](/media/?type=post&id=85439&key=user_uploads/684233/c914554d-0323-4701-8892-d9bf859a832c)\n\nNithin and John are huge AI + Bio nerds who:\n\n* Met 7 years ago competing in International Biology and Neuroscience Olympiads and were roommates at MIT for 4 years\n* Published ML/bio research in NeurIPS, Nature, and PLoS ONE\n\n**The Challenge:**\n\nInformation in biology is incredibly scarce and expensive to validate. While protein AI models like AlphaFold have revolutionized drug discovery, they're still black boxes:\n\n* Companies waste millions testing AI-generated designs and they can't easily control what these models output\n* Limited data makes validation slow and expensive\n\n**Our Approach:**\n\nInstead of trial-and-error, we're leveraging mechanistic interpretability techniques that excel at extracting sparse knowledge from models even with scarce data. We’re demonstrating that the same advances from frontier AI labs in steering models like Claude are applicable to unlock protein language models.\n\nFor our design partners, we’re delivering:\n\n* Direct steering of protein models towards desired properties\n* Interpretable biological features backing every design\n* Efficient exploration of combinatorially massive design spaces with limited data\n\n**See It In Action:**\n\n![uploaded image](/media/?type=post&id=85439&key=user_uploads/684233/3805bd5b-792f-4a8b-bd6c-cffec150612d)\n\nWe're steering Green Fluorescent Protein towards more fluorescent sequences by directly controlling the model's internal knowledge.\n\n**Learn More** at [reticular.ai](http://reticular.ai), our proof-of-concept [blog post,](https://nithin-parsan.medium.com/two-neurons-is-all-you-need-a-case-study-on-interpretability-in-protein-models-5295ab9133cb) and at [demo.reticular.ai](http://demo.reticular.ai).\n\n**We're Looking To Connect With:**\n\n* Startups working with biological language models for design partnerships\n* Pharma teams building generative discovery pipelines\n* Researchers working on AI interpretability\n\nWorking with biological AI or have relevant connections? We'd love to chat! Schedule some time at [calendly.com/reticular](https://www.calendly.com/reticular) or **email us** at [contact@reticular.ai](mailto:founders@reticular.ai)",
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        "title": "Frekil - Scale AI for Medical Scans 🚀",
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        "body": "Hey everyone, we’re [Nikhil](https://www.linkedin.com/in/nikhiltiwari-iitb/) & [Shivesh](https://www.linkedin.com/in/shivesh-gupta-iitb/), co-founders of Frekil!  👋\n\n## TL;DR\n\nFrekil is an end-to-end platform to procure and annotate medical imaging datasets like Xrays, CTs, MRIs, etc 10x faster with AI assistance. We’ve partnered with radiology chains for large-scale imaging data, and have a global marketplace of radiologists for rapid, high-quality annotations.\n\nhttps://youtu.be/2MYjUFp3r0Q?feature=shared\n\n## ❗The Problem\n\nAI is transforming healthcare, but the biggest bottleneck is still **access to medical images from hospitals and annotations by expert doctors**.\n\nHealthcare AI and life sciences companies spend millions collecting medical images and hiring expert radiologists. But annotations? They're still:\n\n📝 Manual\\\n🐢 Slow\\\n🔍 Hard to QA\n\nFinding expert annotators with deep domain knowledge is tough, which means R&D teams waste months and large budgets on a process that should be seamless.\n\nMedical annotation itself is uniquely challenging because it involves:\n\n📦 Gigabyte-scale files\\\n🧠 Complex, multi-dimensional data that’s sensitive to loss\\\n🎯 Accuracy that’s absolutely critical\n\nYet most teams still use open-source desktop tools like 3D Slicer, built decades ago for solo researchers. These tools:\n\n🚫 Require local machines\\\n📉 Offer no real-time collaboration\\\n📊 Force spreadsheet-based coordination\\\n🔐 Risk compliance and data security\n\nAnd the result?\\\nHighly trained radiologists are stuck doing repetitive tasks manually wasting time, slowing innovation, and compromising data quality.\n\n## 🧠 Our Solution\n\nWe transform the way healthcare AI teams prepare data by **accelerating and streamlining the entire annotation pipeline** — cutting timelines from months down to days.\n\nHere’s how:\n\n✅ Deliver fully annotated **medical datasets** tailored for AI research needs\n\n✅ Provide certified and benchmarked **radiologists** for annotation and quality assurance\n\n✅ Offer advanced, browser-based annotation tools for all kinds of medical images—radiology, pathology, histopathology, including X-ray, CT, MRI, ultrasound, etc\n\n✅ Use **AI assistance** to make annotators **10x faster**\n\n✅ Enable customizable clinical workflows with multi-stage reviews & annotations\n\n✅ Ensure **FDA-Ready** annotation versioning, consensus checks, and full audit trails\n\n✅ Track annotator performance in cost, time, and **accuracy** - all built in\n\n### 🤝 The Team\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXctdUlgsBRg891fZ4XU-Ve5trM2bw_h1PYuoQCy0NxrUigEoWMrHYVBO3D3IoAaUC7UPvtDzGxg0C_x2gyO0ujmyJs3_FxtXpLzIVPiUfIZqDKotuf5oeOiGmHyyPRLAzeDm9ORsA?key=Rm0k0m7MflJlURnfdp3zng)\n\nNikhil (left) and Shivesh (right) have been friends since their days at **IIT Bombay**, studying and building together. While working on healthcare AI projects, they experienced firsthand how painful annotation can be especially for large, multi-dimensional medical images that demand extreme accuracy.\n\n[**Nikhil**](https://www.linkedin.com/in/nikhiltiwari-iitb/) (CEO) - Former software engineer at **Stripe, Amazon**, and Marsh McLennan, where he worked on infrastructure, performance optimization, and low-level systems. He graduated from **IIT Bombay** last year, where he led technical initiatives in the student body SARC and built platforms used by 10,000+ students.\n\n[**Shivesh**](https://www.linkedin.com/in/shivesh-gupta-iitb/) (CTO) - Former systems software engineer at **Sony Japan**. He holds an engineering degree from **IIT Bombay**. During his time there, he worked on healthcare AI research and also led the institute’s web and coding club.\n\n## 👋 Our Ask\n\nKnow someone building AI in healthcare whether it’s diagnostic models, clinical trial workflows, or robotic surgery? We’d love to connect.\n\nWe have special offers for academic research, connect us with professors at your university working in healthcare AI!\n\nGot questions or want to chat? Reach out anytime at [founders@frekil.com](mailto:founders@frekil.com) !\n\nOr book a call directly at https://calendly.com/nikhil-frekil/30min ",
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        "title": "SynthioLabs: World's First AI Medical Expert for Pharma GTM Teams",
        "tagline": "We scale your physician engagement - zero headcount added",
        "body": "👋 Hey everyone,\n\nWe’re [Supreet](https://www.linkedin.com/in/supreetdeshpande/), [Raj](https://www.linkedin.com/in/rajashekar-v/) and [Sahitya](https://www.linkedin.com/in/sahitya-sridhar/) - the team behind [SynthioLabs](https://www.synthiolabs.com/), and we’re building the **world’s first AI Medical Expert for Pharma GTM teams**.\n\n**Our Vision:** Reimagine how pharma engages with its most important stakeholders - physicians and patients\n\n**TL;DR**\n\n💡 Our always-on **_AI medical expert_** delivers **_compliant, accurate, and science-first engagement_** to every physician.\n\nWe help pharma GTM teams **3x their physician reach** while ensuring every interaction is medically accurate and personalized.\n\n**🎥 See us in action:**\n\n<https://youtu.be/OoJA5R0y9EQ>\n\n**🚨 Why now?**\n\nPharma’s current model is falling short - despite extensive sales teams, most doctors remain unengaged by pharma\n\n→ **7 out of 10 oncologists** report no meaningful touchpoints with pharma\n\n→ **Long-tail physicians (50–60%)** often receive little to no engagement from pharma reps\n\n→ Many physicians express a **need for deeper scientific insight and relevant medical knowledge**, rather than generic promotional outreach\n\nWith SynthioLabs, you can engage every physician, not just the top tier. No sales reps? No problem, we scale your team instantly.\n\n**🧠 What SynthioLabs does:**\n\n✅ Identifies and screens physicians\n\n✅ Engages in compliant, medical discussions\n\n✅ Educates on clinical guidelines and evidence\n\n✅ Books follow-ups with your medical and sales teams\n\n**📡 The Synthio Polaris Platform for Physician Engagement**\n\nWe’re building more than just one agent. Our platform lets you design intelligent AI agents — medical experts, nurse educators, field partners to **_transform how you engage physicians_**.\n\n**👥 About us**\n\nWe’re ex-McKinsey, ex-ZS, and AI researchers from Amazon.\n\nWe’ve worked with 10 of the top 20 pharma companies and bring over a decade of experience in life sciences, strategy, and AI.\n\n🔍 Want to see it in action?\n\n→ Visit [synthiolabs.com](http://synthiolabs.com)\n\n→ DM us or drop your email to book a demo\n\n→ Know someone in a life science commercial or marketing team? Share this post with them **🙏**\n\n🧠🤝 **Lets redefine how pharma connects with physicians!**\n\n— Supreet, Sahitya and Raj\n\n![uploaded image](/media/?type=post&id=89991&key=user_uploads/2574477/6c23b06e-63e5-4d3f-bfd5-5e65b2a02bfb)\n\n",
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        "title": "Bramante Biologics - American drug manufacturing",
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        "body": "We manufacture pharmaceutical drugs faster than current methods and in the USA. We are building microfactories that can handle the upcoming wave of AI-designed drugs and safeguard against biothreats.\n\n[Launch Video](https://www.youtube.com/watch?v=W_lJOs7k7nI)\n\n### **The Problem**\n\nAmerica is at risk of ceding leadership in advanced biomanufacturing to China.\n\n* **Drug manufacturing is moving to China;** 79% of US biotech and pharma companies already have at least one contract with a Chinese firm, _primarily because they are faster._\n* **China is investing billions in biotech**; one third of new compounds licensed by US pharma are now _discovered and manufactured_ in China, up from almost none five years ago.\n* **Over-reliance on foreign biomanufacturing is a national-security risk;** agile, on-shore manufacturing is critical to protect our nation against future biothreats.\n\n### **Our Solution**\n\nA contract development and manufacturing organization (CDMO) for advanced drugs, rebuilt from the ground up to address the future of America’s biomanufacturing needs.\n\n* **Faster biomanufacturing**; Our platform leverages advanced methods to accelerate production timelines and de-risk scale-up.\n* **Modular container factories**; our form factor is a standard shipping container that can be brought up to operation in roughly two weeks and shipped where it is needed.\n* **Supply-chain independence;** by vertically integrating and manufacturing critical equipment in-house, we secure production against global disruptions.\n\n### **Why It Matters**\n\n* **Drugs can be developed faster**; shorter production cycles move life-saving drugs from the lab to the clinic up to a year sooner.\n* **More drugs can be developed**; we will be able to manufacture the thousands of low-volume, personalized biologics that will be designed by AI.\n* **Strengthening US autonomy;** rapid-deploy, U.S.-based capacity protects the nation from supply shocks, geopolitical turbulence, and potential bioweapons.\n\n### **Who We Are**\n\nWe love biomanufacturing. Together, we built Protein Evolution, an industrial biotech company that turned plastic waste into new textile fibers. Scott is ex-McKinsey and a Yale mathematician (he loves a good cost model almost as much as he loves America). Mike earned his PhD in Bioengineering from Penn after completing Penn’s M&T program in undergrad (he builds DNA, software, and bioreactors).\n\n### **Our Ask**\n\n**Please reach out** if you work in drug development, work at a CDMO, or have experience working with or at the FDA: [**founders@bramantebio.com**](mailto:founders@bramantebio.com)\n\n**Follow along** on our journey:\n\n* [**www.bramantebio.com**](http://www.bramantebio.com)\n* [**www.linkedin.com/company/bramante-biologics**](http://www.linkedin.com/company/bramante-biologics)\n\n![uploaded image](/media/?type=post&id=90667&key=user_uploads/547597/1ed9b2f2-f6d9-4253-b3b0-f7561d246977)\n\n_Let’s build the infrastructure tomorrow’s medicine deserves, right here at home._",
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        "title": "💻 AgentHub 📈 - The Staging Environment for your AI Agents",
        "tagline": "Simulate, trace, and evaluate agent behavior in real-world environments",
        "body": "### **TL;DR**\n\nAgentHub is an implementation-agnostic platform for evaluating AI agents in realistic simulation environment sandboxes. Use cases that we support include computer-use, browser, conversational, and tool-use agents. We help teams test, trace, track, and improve agent behavior at scale before deployment, closing the gap between vibe checks and real-world performance.\n\n👉 Check us out [AgentHubLabs.com](http://AgentHubLabs.com)\n\n💬 If you’re building agents, training models, or can connect us to someone who is, we’d love to [chat](https://calendar.google.com/calendar/appointments/schedules/AcZssZ1FO0fw7yHEhAAHWoiyCOS2umxQTuat9bIUwOOzomfV4D8bxhKakigP0nglXPAQh-4qvrYwukiM)!\n\n---\n\nhttps://www.youtube.com/watch?v=1gPVYcphmmY\n\n### **🧩 The Problem**\n\nLLM-based agents often **pass simple evals at a small scale and in specific cases** but **fail in deployment**. They hallucinate tool use, misuse APIs, or follow an inefficient (or incorrect) reasoning trajectory that isn’t captured in standard benchmarks. There’s no standard way to evaluate agents beyond prompt-based tests - especially across **tool use**, **UI interactions**, and **multi-step reasoning**.\n\n---\n\n![uploaded image](/media/?type=post&id=92664&key=user_uploads/1435098/77c2d173-7349-41e4-90a2-e2b69712ede5)\n\n### **✅ The Solution: AgentHub**\n\nAgentHub lets you **simulate real-world environments** and **evaluate agents end-to-end** using structured tasks, traces, and grading:\n\n* 🧪 **Multi-domain, customizable environments for**: e-commerce, CRM, filesystem, browser agents, dashboards, and more\n* 🕵️ **Full tracing** in standardized OpenTelemetry format: capture every LLM/tool call, decision point, and UI step\n* 📊 Evaluation that’s easy to plug and play: LLM, human-in-the-loop, and rule-based grading + custom metrics at the task, step, or tool level\n* 🛠️ **Debug and iterate fast**: See exactly where agents fail, and improve reasoning or control loops\n* 🧠 **Automated Insights**: Get automatic insights into failure modes and auto-generate suggested fixes\n\nWhether you’re testing autonomous workflows, tool-using copilots, or browser agents, we give you a **sandboxed, realistic playground** to evaluate in before you ship.\n\n---\n\n![uploaded image](/media/?type=post&id=92664&key=user_uploads/1435098/a2b71d28-7310-4423-ac4d-1c717f244af4)\n\n### **🕵️ Who We Are**\n\nYoussef was previously a tech lead on the Foundation Model Evaluation team at Apple and studied CS at CMU.\n\nSandra has been an engineer at multiple startups, Google, and Meta, and studied CS and design at MIT.\n\nWe’ve been friends since a few years ago through a summer spent in Seattle and are obsessed with building the missing critical infrastructure to help teams **evaluate, debug, and trust** their agents.\n\n---\n\n### **🙌 How to Help / Get Involved**\n\n* Learn more and book a demo: [AgentHubLabs.com](http://AgentHubLabs.com)\n* We’re looking for teams and labs working on agents in:\n  * tool-use\n  * browser automation\n  * conversational chat\n  * and computer-use\n* Have an eval challenge? We’ll help you simulate it.\n* Use agents in prod? It’d be bananas to not test them on AgentHub first 🍌\n\n![uploaded image](/media/?type=post&id=92664&key=user_uploads/89898/9cf2b39b-039e-45a4-b4a6-969310c4378e)\n\n",
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    "long_description": "AI is making biology easy to engineer. Life-saving drugs and therapies are accelerating, but so is the ability for bad actors to create a pandemic. TwentyTwo is working on the defensive stack to prevent the next pandemic.",
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    "subindustry": "Healthcare -> Industrial Bio",
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        "full_name": "Evan Seeyave",
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        "title": "Mohi: Debugging assistant for AI agents",
        "tagline": "Don’t know why your agent messed up? Neither do we. But Mohi will show you exactly where your agents went wrong - and why.",
        "body": "Hey folks! 👋\n\nWe’re building Mohi, a debugging assistant for AI agents. If you've ever spent hours scrolling through 500+ LLM calls asking “what the hell happened?” — this is for you.\n\nhttps://youtu.be/UzQZlUdZ59s\n\n### The Problem\n\nAgents are complicated — tools, memory, web search, nested loops, and hallucinations. And when something breaks, it’s like untangling spaghetti. You’re stuck scrolling through flat logs, guessing which of the _35_ steps silently failed.\n\nIt's slow, brittle, and makes you second-guess your whole stack.\n\n### **The Fix**\n\nMohi makes debugging agents **_fast_**, and **_actually usable_**.\n\nJust tag key steps in your agent with our lightweight SDK (think: tool calls, memory access, context switches, decision logics), and we build a clean visual graph of what happened under the hood. Then we go a step further and show you:\n\n* where your agent last succeeded\n* where it went off the rails (hallucinated, failed tool, wrong branch)\n* and how to re-run broken steps with new inputs\n\nIt’s not just observability. It's a diagnosis.\n\n### **Who We Are**\n\nPreviously, Hao worked on AI and distributed systems observability at AWS, and Evan worked on automated testing at Google and AI evaluations at SecureBio. We met at MIT and worked on a bunch of projects together, eventually building agents ourselves and seeing firsthand how painful debugging can be — so we built Mohi to fix it.\n\n![uploaded image](/media/?type=post&id=92831&key=user_uploads/1675765/aa1f6344-8ce0-4ad5-a5ba-0e203412791a)\n\n### **Our Ask**\n\nIf you’re building complex agents — we’d love to chat!\n\n→[ https://trymohi.com/](https://trymohi.com/)\n\n→ [founders@trymohi.com](mailto:founders@trymohi.com)",
        "url": "https://www.ycombinator.com/launches/O9H-mohi-debugging-assistant-for-ai-agents",
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        "full_name": "Ayaan Parikh",
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        "full_name": "Rahul Vijayan",
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        "id": 92466,
        "title": "Convexia: World’s First AI-Maximalist Pharma Company",
        "tagline": "AI agents that search and evaluate early-stage drugs",
        "body": "Hey 👋 We’re [Ayaan](https://www.linkedin.com/in/ayaan-parikh/) and [Rahul](https://www.linkedin.com/in/rahulvijayan28/) — Stanford CS dropouts who’ve exited 3 startups together.\n\nOur 4th startup is [Convexia](https://www.convexia.bio/), an AI-maximalist pharma company. We buy drugs, run clinical trials, and then sell for profit.\n\n🎥 [Launch Video](https://www.youtube.com/watch?v=oFaM-ceSGok)\n\n# **Problem**\n\nPharma is the most bloated, antiquated trillion-dollar industry on the planet.\n\nLife-saving drugs are overlooked, abandoned, or shelved because diligence is still done by hand. Slow, costly, and outdated.\n\n# **Our Solution**\n\nWe use agents to operate 10x faster and 20x leaner. Convexia’s AI agents autonomously handle:\n\n* **Sourcing** – Find hidden early-stage drugs from around the world.\n* **Scientific diligence** – Evaluate drug viability with leading computational biology tools\n* **Market analysis** – Model market size, competition, and IP positioning\n* **Clinical operations** – Simulate trials, flag CMC issues, and discover CROs\n* **Business development** – Match each drug to the right buyer\n\nTest our agents here: https://www.convexia.bio/playground \n\n![uploaded image](/media/?type=post&id=92466&key=user_uploads/2433930/cb571daa-a456-472c-a8fe-b370b2e3b50e)\n\n# **Our Ask**\n\nBefore buying and selling drugs ourselves, we are running pilots with early customers who want to accelerate their search and evaluation processes.\n\n📅 Book a call [here](https://calendly.com/ayaan-convexia/demo). \n\nHere’s who we’re looking to meet—see if anyone’s in your network [here](https://www.linkedin.com/search/results/people/?currentCompany=%5B%22434006%22%2C%2221263%22%2C%2211127960%22%2C%2277579063%22%2C%2218268402%22%2C%223204230%22%2C%2234756183%22%2C%222673956%22%2C%2210317593%22%2C%2286884606%22%2C%2238336%22%2C%2219031898%22%2C%224990022%22%2C%221337177%22%2C%228013%22%2C%2226195%22%2C%226761%22%2C%22617961%22%2C%2212150%22%2C%2248171%22%2C%2211647%22%2C%226866%22%2C%2213476%22%2C%225239%22%2C%227635%22%2C%2233408697%22%2C%223235%22%2C%221893%22%2C%225482%22%2C%222068%22%2C%221663%22%2C%221304385%22%2C%221773%22%2C%221603%22%2C%221399%22%2C%2291149342%22%2C%221642%22%2C%2216233341%22%2C%221485%22%2C%221486%22%2C%222227%22%2C%221406%22%2C%222276%22%2C%221602%22%2C%221204%22%2C%222281734%22%2C%2229088045%22%2C%221185%22%2C%229215024%22%5D&industry=%5B%2215%22%2C%2212%22%5D&network=%5B%22F%22%5D&origin=FACETED_SEARCH&sid=a*t).\n\n* Small/mid pharma companies (Incyte, Nimbus, Roivant SubCos)\n* Biotech VCs (Versant, Atlas, RA Capital)\n* Computational biotechs (Recursion, Insitro, Owkin)\n* Operators who’ve brought drugs to market\n\n# Reach us: [founders@convexia.bio](mailto:founders@convexia.bio)",
        "url": "https://www.ycombinator.com/launches/O3O-convexia-world-s-first-ai-maximalist-pharma-company",
        "votes": 34,
        "created_at": "2025-07-26T01:34:30.747Z"
      }
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    "id": 30856,
    "name": "Scoop",
    "slug": "scoop",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/70fc9c8245a9de2b245841faf07e0530dd3f51a1.png",
    "website": "https://scooplabs.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We’re building AI agents that speed up drug trials by automating the manual consolidation and document prep required for every IND submission. Today, biotechs spend months stitching together reports from contractors and internal teams just to file and get to their first-in-human trial.",
    "one_liner": "Automating regulatory submissions for life sciences",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B",
    "launched_at": 1762725446,
    "tags": [
      "B2B",
      "Biotech",
      "Compliance",
      "AI",
      "Biotechnology"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2025",
    "status": "Active",
    "industries": [
      "B2B"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/scoop",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/scoop.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/scoopcpg/",
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    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/70fc9c8245a9de2b245841faf07e0530dd3f51a1.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Tom Blomfield",
      "url": "https://www.ycombinator.com/people/tom-blomfield"
    },
    "founders": [
      {
        "user_id": 822396,
        "full_name": "Vincent Park",
        "title": "Founder/CEO",
        "founder_bio": "CEO and Co-founder @ Scoop. Previously at Persona and Dropbox.\n\nWorking on Scoop to help speed up drug trials, starting with IND filings, to help companies bring life-saving treatments to market faster.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/vpark",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/689facc831f3923fa62a1e4575b13dab3e406e64.jpg"
      },
      {
        "user_id": 476131,
        "full_name": "Jay Kwon",
        "title": "Founder/CTO",
        "founder_bio": "Co-founder and CTO @ Scoop.\n\nEx-Google Search applied AI (Gemini CoT), supported IND and Phase III clinical trial design for ALS therapy at Corestem, prev at Genentech and CJ Bioscience. MS in Computer Science and Biotechnology from Penn.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/soonjae/",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c6d4e3bea7c64f6ca250ea094b791ae99f673df2.jpg"
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    "launches": [
      {
        "id": 95349,
        "title": "Scoop: AI agents that speed up drug trials, starting with IND filings",
        "tagline": "We create ready-to-submit regulatory documents so new therapies can reach clinical trials faster",
        "body": "Hey everyone, we’re Jay and Vincent.\n\ntl;dr: Scoop uses AI to automate the paperwork required to start and maintain human drug trials, reducing timelines from months to days.\n\n![uploaded image](/media/?type=post&id=95349&key=user_uploads/476131/f682e615-6b6a-487a-b532-58158884d410)\n\n**The Problem**\n\nBiotechs spend months preparing the documentation required to start human trials. Most of the time goes into organizing results, rewriting information across many documents, and keeping everything consistent.\n\nCompanies often hire Contract Research Organizations(CROs), which run clinical trials and also help prepare these submissions. But their document assembly process is still manual and slow, so timelines stretch and costs rise.\n\nEvery update requires repeating the same work. This delays trials and wastes $$$ before a single patient is treated.\n\n**The Solution**\n\nScoop turns your internal data and study reports into a continuously updated submission workspace.\n\nOur system generates the draft documents in the required structure, keeps everything consistent when new data comes in, and flags anything that needs review. Teams focus on approving content, not assembling it.\n\nWhat used to take months can now be completed in days.\n\n**Impact**\n\nWe're compressing IND assembly from the order of months to days. For a typical biotech, that means:\n\n* Getting to first-in-human trials 2-5 months faster\n* Saving $100K+ in CRO fees per submission\n* Reducing rejection risk through automated compliance checks\n\n**About Us**\n\n![uploaded image](/media/?type=post&id=95349&key=user_uploads/476131/5591f53a-d15a-449b-857d-c723e3535665)\n\nWe met at UPenn 16 years ago (yes, it’s been a minute) and have been best friends ever since.\n\nVincent led AI-powered document processing for Fortune 10 companies at Persona, automating KYB/KYC workflows at massive scale. Previously, he was a tech lead at Dropbox.\n\nJay was a tech lead in Google Search AI and previously worked at biotechs including Genentech, and has helped file INDs and led statistical design work for a Phase 3 ALS clinical trial. He holds two MS degrees in both Biotechnology and Computer Science.\n\nWe've seen both sides of this problem: the AI capabilities that can solve it and the regulatory complexity that makes it hard.\n\n**Our Ask**\n\nWe’d love warm introductions to regulatory leads at biotechs or CROs who want to accelerate IND preparation and modernize their submission workflows.\n\n📨 [founders@scooplabs.ai](mailto:founders@scooplabs.ai)\n\nThanks!",
        "url": "https://www.ycombinator.com/launches/Ont-scoop-ai-agents-that-speed-up-drug-trials-starting-with-ind-filings",
        "votes": 53,
        "created_at": "2025-11-10T02:17:36.817Z"
      }
    ]
  },
  {
    "id": 30925,
    "name": "Paceline Bio",
    "slug": "paceline-bio",
    "former_names": [
      "Play Health"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/52ae3355c425a5e6bfaad9876e3d186dff23a243.png",
    "website": "https://paceline.bio/",
    "all_locations": "Boulder, CO, USA",
    "long_description": "Paceline Bio runs the research and clinical ops layer behind biotech and pharma company who want to move fast, starting with biospecimen collection, logistics, and storage. We're built to get you from scope to signature in just one day so you can accelerate your programs.",
    "one_liner": "Biospecimen logistics & operations layer to accelerate drug dev",
    "team_size": 3,
    "industry": "Healthcare",
    "subindustry": "Healthcare",
    "launched_at": 1760457684,
    "tags": [
      "AI-powered Drug Discovery",
      "Biotech",
      "Drug discovery",
      "Biotechnology"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2025",
    "status": "Active",
    "industries": [
      "Healthcare"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/paceline-bio",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/paceline-bio.json",
    "year_founded": 2025,
    "city": "Boulder",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/paceline-bio/",
    "x_url": "https://x.com/playhealthco",
    "fb_url": null,
    "cb_url": null,
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/52ae3355c425a5e6bfaad9876e3d186dff23a243.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [],
    "jobs": [],
    "news": [],
    "launches": []
  },
  {
    "id": 30937,
    "name": "Anto Biosciences",
    "slug": "anto-biosciences",
    "former_names": [
      "Anto"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/83f56c369ac0fb5e70438516e774b8603717940a.png",
    "website": "https://www.anto.bio/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Anto is building a foundation model for microbial communities, making the gut microbiome computable for the first time. We predict drug toxicity and efficacy across diverse populations and fix drugs so they work for everyone — solving the hidden root cause of most drug failures. \r\n\r\nFounded by Arvid (Broad Institute of MIT and Harvard; Nature-published researcher who pioneered quality-aware, goal-directed sparsification) and David (Harvard Medical School Gastroenterology, J&J), second-time founders who published the breakthrough at leading machine learning AI conferences and Nature. ",
    "one_liner": "A Foundation Model for Microbial Communities.",
    "team_size": 4,
    "industry": "Healthcare",
    "subindustry": "Healthcare",
    "launched_at": 1761948352,
    "tags": [
      "Generative AI",
      "Biotech",
      "Genomics",
      "Drug discovery",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2025",
    "status": "Active",
    "industries": [
      "Healthcare"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/anto-biosciences",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/anto-biosciences.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/anto-biosciences/",
    "x_url": "https://x.com/anto_f25",
    "fb_url": null,
    "cb_url": null,
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    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/83f56c369ac0fb5e70438516e774b8603717940a.png",
    "app_video_url": null,
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    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 1514534,
        "full_name": "Arvid E. Gollwitzer",
        "title": "Founder/CEO",
        "founder_bio": "Founder @ Anto Bio | Researcher @ Broad, Harvard, MIT | Lecturer @ ETH Zurich",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/arvid-gollwitzer/",
        "x_url": "https://x.com/arvidgollwitzer",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c198f2ae79729c0ec59fbf566666e42e58082db3.jpg"
      },
      {
        "user_id": 1361118,
        "full_name": "David de Gruijl",
        "title": "Founder/CTO",
        "founder_bio": "Co-founder @ Anto. Formerly Janssen Pharmaceuticals, ETH Zurich and Gastroenterology Dept. of BWH, Harvard Medical School. ",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/daviddegruijl",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b50bd83ae243af5006498387e9733112e4af827c.jpg"
      }
    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 95292,
        "title": "Anto Biosciences: A Foundation Model for Microbial Communities",
        "tagline": "Making the microbiome computable. ",
        "body": "We’re [Arvid](https://www.linkedin.com/in/arvid-gollwitzer/) and [David](https://www.linkedin.com/in/daviddegruijl/), and we’re building [Anto Biosciences](https://www.linkedin.com/company/anto-biosciences).\n\n### tl;dr\n\nAnto is building a multimodal foundation model for microbial communities, making the gut microbiome computable for the first time. We predict drug toxicity and efficacy across diverse populations and optimize molecules for universal efficacy — addressing the microbiome-driven causes of drug response and failures.\n\n**Launch Video**: https://youtu.be/BDPp2wXiDbw\n\n### The Key Problem\n\n**We caught a cancer drug that worked in China but failed in U.S. trials. Same molecule, different microbiomes. $1B lost.**\n\nThis isn't an isolated story; more than a billion people are currently on medications where the microbiome dictates success or failure. The gut microbiome drives drug toxicity and efficacy, but pharma lacks the tools to understand and act on it \\[10,11,13\\]. Current methods to simulate the gut take months, cost millions, and rely on animal models that are too different from humans to be useful.\n\n### About Anto\n\n**We predict drug toxicity and efficacy across diverse populations and optimize molecules for broader efficacy—addressing the hidden, microbiome-driven causes of drug response and failures.**\n\nInput any drug molecule. We tell you how people will respond. We show where it works and where it fails—by geography, age, or diet.\n\nWe predict:\n\n* Population-specific efficacy (geographic, age-based, vegans vs. meat eaters, etc.)\n* Failure mechanisms (specific microbial metabolic pathways) \\[9,11\\]\n* Treatment response profiles across diverse populations \\[10,14\\]\n\nAnd we don't just flag risk; we identify failure mechanisms down to microbial metabolic pathways and generate treatment response profiles across populations. Then we optimize the molecule for broader efficacy. Turning 20% efficacy into 80% becomes an optimization problem we can actually solve.\n\n**Why we can do this:** We've pioneered quality-aware, goal-directed sparsification algorithms \\[5,9,15\\]. Microbiome data is 99% noise. We remove most of the noise and low-quality data through sparsification, leaving only the 1% that actually carries predictive power. That's how we make the microbiome computationally tractable. \\[6,7,8,14\\].\n\n### About Us\n\n[Arvid](https://www.linkedin.com/in/arvid-gollwitzer/) — Broad Institute of MIT and Harvard, ETH Zurich. Nature-published researcher who pioneered microbiome sparsification. Built the methods that make noisy microbial data tractable.\n\n[David](https://www.linkedin.com/in/daviddegruijl/) — ex-ETH Zurich, J&J, BWH, Harvard Med, Gastroenterology. Struggled with current drug digestion models firsthand. Knows where current workflows break and how to fix them.\n\n![uploaded image](/media/?type=post&id=95292&key=user_uploads/89898/3b2d64c2-dbc0-4bad-aad7-40b7995faeca)\n\nWe’re second-time founders who have lived this problem and published at leading AI conferences and Journals.\n\n### Traction\n\nWe're working with pharma R&D to simulate drug–microbiome interactions and de-risk cross-border deals with population-specific efficacy predictions and response profiles. Our open-source tools and research \\[1-9,14,15\\] are widely adopted and taught in engineering and computational biology programs \\[16\\].\n\n### What This Means for Digital / Computational Biology\n\n**The microbiome is the missing layer in drug development.** Most orally administered drugs are modified by gut bacteria before reaching their targets. We're making this layer computable for the first time \\[11-14\\].\n\nIf you're building in digital biology, precision medicine, or drug development, let's talk. The microbiome affects everything.\n\n### Our Asks\n\nIf you’re in drug development, cross-border deals, or translational medicine, we’d love to learn what microbiome requirements matter most to you. If you know teams in pharma and believe this product is cool, sharing / intros would mean a lot.\n\nIf you are in academia or microbiome research, try out our open-source tools \\[5-15\\]!\n\n**Get in touch**: [founders@anto.bio](mailto:founders@anto.bio) | [anto.bio](https://www.anto.bio/)\n\n### Our Research\n\nOur foundational work spans high-performance genomic analysis \\[1-4\\], metagenomic classification algorithms \\[5,9,14,15\\], in-storage computing for microbiome data \\[6-8\\], and the first large-scale datasets for causal modeling of microbial ecosystems \\[11,12\\]. We've pioneered techniques that make microbial communities computationally tractable, including quality-aware sparsification that reduces noise by 99% while preserving predictive power \\[5,9,15\\]. Our recent work on therapeutic resistance \\[10\\] and AI-controlled metagenomics for clinical applications \\[14\\] directly informs our drug optimization capabilities.\n\n**References:**\n\n\\[1\\] N. M. Ghiasi et al., \"GenStore: A high-performance in-storage processing system for genome sequence analysis,\" in _Proc. 27th ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems_, 2022, pp. 635-654.\n\n\\[2\\] N. M. Ghiasi et al., \"GenStore: In-storage filtering of genomic data for high-performance and energy-efficient genome analysis,\" in _2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)_, 2022, pp. 283-287.\n\n\\[3\\] N. M. Ghiasi et al., \"GenStore: A high-performance and energy-efficient in-storage computing system for genome sequence analysis,\" _arXiv preprint arXiv:2202.10400_, 2022.\n\n\\[4\\] M.-D. Rumpf, M. Alser, A. E. Gollwitzer et al., \"SequenceLab: A comprehensive benchmark of computational methods for comparing genomic sequences,\" _arXiv preprint arXiv:2310.16908_, 2023.\n\n\\[5\\] A. E. Gollwitzer et al., \"MetaTrinity: Enabling fast metagenomic classification via seed counting and edit distance approximation,\" _arXiv preprint arXiv:2311.02029_, 2023.\n\n\\[6\\] N. M. Ghiasi et al., \"MetaStore: High-performance metagenomic analysis via in-storage computing,\" _arXiv preprint arXiv:2311.12527_, 2023.\n\n\\[7\\] N. M. Ghiasi et al., \"MetaStore: High-performance metagenomic analysis via in-storage computing,\" _arXiv e-prints_, pp. arXiv-2311, 2023.\n\n\\[8\\] N. M. Ghiasi et al., \"MegIS: High-performance, energy-efficient, and low-cost metagenomic analysis with in-storage processing,\" in _2024 ACM/IEEE 51st Annual Int. Symp. Computer Architecture (ISCA)_, 2024, pp. 660-677.\n\n\\[9\\] A. Gollwitzer et al., \"MetaFast: Enabling fast metagenomic classification via seed counting and edit distance approximation,\" _arXiv preprint arXiv:2311.02029_, 2023.\n\n\\[10\\] A. E. Gollwitzer, D. A. Subramanian, I. Tucker, and G. Traverso, \"Steering the evolutionary game: Hierarchical control of therapeutic resistance in cancer treatment,\" in _NeurIPS 2025 AI for Science Workshop_, 2025.\n\n\\[11\\] A. E. Gollwitzer, D. A. Subramanian, I. Tucker, and G. Traverso, \"MetaOmics-10T: The foundational dataset to unlock causal modeling of microbial ecosystems,\" in _NeurIPS 2025 AI for Science Workshop_, 2025.\n\n\\[12\\] N. M. Ghiasi et al., \"MegIS: High-performance, energy-efficient, and low-cost metagenomic analysis with in-storage processing,\" _arXiv preprint arXiv:2406.19113_, 2024.\n\n\\[13\\] H. J. Haiser and D. de Gruijl, \"Emerging tools and technologies for microbiome-aware drug development,\" _Clinical Pharmacology & Therapeutics_, 2025, doi:10.1002/cpt.70026.\n\n\\[14\\] A. Gollwitzer et al., \"AI-controlled metagenomics: High-throughput taxonomic profiling for clinical pathogen detection and early-stage cancer screening and type classification,\" _arXiv preprint arXiv:2311.02029_, 2025.\n\n\\[15\\] A. Gollwitzer et al., \"The Thinking Microscope: A reinforcement learning framework for the co-optimization of computational and generative data sparsification in metagenomics,\" _arXiv preprint arXiv:2311.02029_, 2025.\n\n\\[16\\] A. E. Gollwitzer, \"Decoding the human microbiome through AI-controlled metagenomics: Promises and ethical implications,\" presented at ETH Zurich ETHix Series, Dec. 2024. \\[Online\\]. Available [here](https://bioethics.ethz.ch/news-and-events/news-bioethics/2024/12/decoding-the-human-microbiome-through-ai-controlled-metagenomics-promises-and-ethical-implications.html).",
        "url": "https://www.ycombinator.com/launches/Omy-anto-biosciences-a-foundation-model-for-microbial-communities",
        "votes": 32,
        "created_at": "2025-11-07T20:30:08.883Z"
      }
    ]
  },
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    "id": 31064,
    "name": "Origin",
    "slug": "origin-bio",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3f0bdde9261c1483c617c9ca235a7378e3d7ba10.png",
    "website": "https://origin.bio/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We produce patient-derived cancer datasets for licensing to pharmaceutical and AI companies. Our data comprises matched spatial transcriptomics, H&E and clinical labels. We operate our own laboratory, where samples are processed. Samples are sourced from leading biobank / hospital networks.\r\n\r\nAs R&D workflows increasingly rely on frontier AI systems, we believe there is critical need for real world datapoints so that findings translate in the clinic.",
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        "body": "\n![uploaded image](/media/?type=post&id=97480&key=user_uploads/552024/dd558bc0-b110-466f-b603-d3acb473ba54)\n\nHey everyone!\n\nWe’re Yash and Malhar from Origin.\n\n**TL;DR**\n\n* Using AI to make cell & gene therapies **safer** by designing **DNA switches and dials**.\n* Our model’s synthetic regulatory sequences program precise gene expression patterns in disease cells.\n* We’re building the **largest** **proprietary dataset** consisting millions of experimentally validated regulatory DNA sequences across many different cell and tissue types.\n* Our model **outperforms Google DeepMind’s AlphaGenome** at predicting regulatory element activity.\n\n<https://youtu.be/jCN43gOy7x8?si=pkZU6sGREgiI-EHA>\n\n**The Promise**\n\nCell and gene therapies have successfully been used to treat diseases like stage IV leukemia, retinal disease, and spinal muscular atrophy. However, their application is limited due to concerns around safety and efficacy. But this drug class holds the promise of being a **durable cure** for diseases like cancer, autoimmune, neurodegenerative, aging, and more.\n\n**The Problem**\n\nToxicity events largely arise due to our inability to control **where**, **when,** and to **what** extent genes are turned on. Regulatory DNA sequences - enhancers and promoters - function to modulate the expression of genes. Current approaches to design these sequences involve naive methods like repeats of known motifs (patterns) and random ligation. These methods either **reliably yield poor candidates** or require **brute force to search** for even one promising hit.\n\n<https://youtu.be/SDJFAJULTP0?si=IYXFHLAqU88CcaXK>\n\n**Our Solution**\n\nAxis – our state-of-the-art model – is trained to design and screen regulatory DNA to activate therapeutic genes in target disease cell-states.\n\nAxis is able to rapidly sample **millions** of functional candidates when prompted by the cell-state. We’re currently testing these sequences in the lab to produce the **largest proprietary dataset** of synthetic regulatory elements, measuring their interaction with transcription factors and the phenotypic outcomes of gene expression.\n\n**Axis’ sequences have the highest predicted expression in targeted cell types**\n\nUsing Malinois, an independent and in-vitro validated prediction model by the Broad Institute, we observed that Axis’ generated sequences had the highest transcriptional activity in the cell type they were prompted by.\n\n![uploaded image](/media/?type=post&id=97480&key=user_uploads/552024/deb58343-aec7-4554-a176-d4ff1927da9f)\n\n**Axis’ sequences contain known biological features**\n\nRegulatory DNA sequences are activated when proteins called transcription factors bind to them at specific patterns of ATGCs known as motifs.\n\nHNF4A is a known master regulator of hepatocyte and liver gene expression. We observed that in the generated sequences, its known motif most frequently occurred in HepG2 (liver cell line) targeted sequences.\n\n![uploaded image](/media/?type=post&id=97480&key=user_uploads/552024/d4e9dc1a-47c7-4599-99f3-f0b4d8980c6a)\n\n**Axis’ sequences have high diversity and explore a large design space**\n\nOver 72% of our generated sequences have no BLAT alignment match up amongst themselves (using a 20bp threshold).\n\n![uploaded image](/media/?type=post&id=97480&key=user_uploads/552024/45c66f66-c144-46ac-aea9-2739e6aae6d9)\n\n**Axis outperforms Google DeepMind’s AlphaGenome**\n\nAxis demonstrates strong performance in predictive benchmarks measuring molecular properties of real regulatory sequences. These include measures like chromatin accessibility and transcription factor protein binding.\n\n![uploaded image](/media/?type=post&id=97480&key=user_uploads/552024/f1b68778-ea47-44b1-a2fc-ee4ac39c25bf)\n\n**The ask:**\n\nIf you know or are someone working on partnerships at pharmaceutical / biotech companies working in cell & gene therapies, reach out to explore:\n\n* Partnering on regulatory sequence designs for target disease cells.\n* Getting access to our proprietary dataset of sequence properties, sequence-protein interactions, and cell-state perturbations.\n\n\\\nFeel free to reach out via email at [yash@origin.bio](mailto:yash@origin.bio)",
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        "created_at": "2026-02-04T21:33:23.276Z"
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        "title": "AI is spitting out more potential drugs than ever. This startup wants to figure out which ones matter. | TechCrunch",
        "url": "https://techcrunch.com/2026/04/22/ai-is-spitting-out-more-potential-drugs-than-ever-this-start-up-wants-to-figure-out-which-ones-matter/",
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        "title": "10x Science: Unlocking the future of drug development 🔑🧬",
        "tagline": "The AI-native platform for next-generation protein characterization",
        "body": "Hi Everyone 👋:\n\nWe're [**David**](http://www.linkedin.com/in/davidstephenroberts), [**Andrew**](https://www.linkedin.com/in/andrew-hawthorn-reiter/), and [**Vishnu**](https://www.linkedin.com/in/vishnutejus/), co-founders of [**10x Science**](https://10xscience.com/). We’re a team of researchers from Stanford on a mission to make drug development 10x faster.\n\n**TL;DR:** We are building _the_ AI-native solution to unlock fast, accurate, and scalable therapeutics characterization to accelerate drug development.\n\n[10x Science Launch Video](https://www.youtube.com/watch?v=WaQSdNSc1KM)\n\n❌ **The** **Problem**\n\nProtein-based drugs now make up a majority of new therapies and advances in AI are flooding the drug discovery pipeline with more candidates than ever. But **the bottleneck isn't candidate design, it’s candidate characterization to identify the best drug for clinical trials**. \n\nEvery antibody, every cell therapy, every engineered protein requires characterization at a level of molecular detail that existing tools simply weren't designed to handle. Today, that process is: \n\n* **Slow**: Weeks to months per candidate spent on time-consuming data analysis and report generation that limit how fast new therapies can reach patients \n* **Expensive**: On average, it costs $1M+ for each day of delay in a clinical trial. Every day a biologic is delayed from entering the market is a missed opportunity to generate revenue and to save lives\n* **Not scalable**: Labs are throwing thousands of therapeutics through the discovery pipeline, but processing the data requires individuals to perform expert analysis leading to reproducibility challenges and a specialized, manual bottleneck\n\nThe result: candidates pile up, timelines slip, and the promise of AI-powered discovery gets stuck. \n\n💡**Our Solution**\n\n**10x Science** is the unlock **🔑**\n\nWhat if you could go from raw data to output-ready characterization reports in minutes? That’s what we’re building: **AI-native infrastructure for protein characterization**:\n\n* **Reduce analysis time from weeks to minutes**: our platform automates the data crunching that currently bottlenecks development → yielding $150,000+ time savings per team / month\n* **Get reproducible, enterprise-ready results** without requiring extensive expertise and slow manual curation. This frees scientists to focus on the important stuff: discovery, not number crunching\n* **Scale your pipeline**: process more candidates exponentially without linearly scaling your team\n\nNow, therapeutics development can keep pace with AI-powered drug discovery.\n\n![uploaded image](/media/?type=post&id=97760&key=user_uploads/3203252/9b62279d-80f7-4f58-a80c-86665a72b6d7)\n\n**📜 Backstory**\n\nWe've lived this problem.\n\nAs Stanford researchers building frontier tools at the intersection of chemistry, biology, and machine learning, we constantly hit the protein characterization throughput wall. We and every other scientist we’ve spoken with all feel the same pain: protein characterization is slow, manual, and just hard to do at scale. Today’s scientists are drowning in data. A single sample can generate gigabytes of data, yet extracting insights from it is painstakingly slow without the right tools.\n\nIf we don’t modernize protein analysis, then drug development grinds to a halt. **This is why we started 10x Science**.\n\n🔬 **Our Team**\n\nWe are the only team in the world with deep expertise in proteomics, mass spectrometry, chemical biology, and artificial intelligence to tackle this problem. We’re researchers from Stanford with 18+ years of hands-on experience characterizing biologics and building new technologies and methodologies at the cutting edge. \n\n![uploaded image](/media/?type=post&id=97760&key=user_uploads/3203252/a384189a-2f27-4969-8d8f-156305c9ce8d)\n\nDavid (CEO, middle): Damon Runyon Cancer Research Foundation Fellow; Stanford Postdoc from Carolyn Bertozzi’s Lab (2022 Nobel Prize in Chemistry); early career leader in field of mass spectrometry-based proteomics with 37+ peer-reviewed publications\n\nAndrew (COO, right): Trained in Steven Carr’s Proteomics Platform Lab at the Broad Institute of MIT and Harvard, where he developed new methods in mass spectrometry. Later became a Ph.D. student and NSF-GRFP fellow at Stanford University, where he was co-advised by Dr. Carolyn Bertozzi and Dr. Or Gozani.\n\nVishnu (CTO, left): 2x YC founder. Built AI for go-to-market teams as the 2nd hire and founding engineer at Nooks. Forward deployed himself in research labs at the University of Washington, UCSF, and Stanford, where he was mentored by world-renowned scientists in analytical chemistry and cancer research. In &lt; 1 year, he published 2 papers and won 4 national awards. \n\n**We are uniquely positioned** with the technical expertise and insights required to solve this major challenge.\n\n 🌐 **The Big Picture**\n\nWe are automating the whole data analysis pipeline to enable scientists to focus on discovery: this is the \"why\" rather than the \"how\" of data processing. Every major therapeutic breakthrough of the last decade has depended on understanding proteins at the molecular level. And yet, the tools we use to analyze them haven't fundamentally changed in over twenty years. 10x Science is taking a totally new approach to usher in an AI-forward paradigm in protein characterization and drug development. \n\n 🙏 **Our Ask**\n\nWe're onboarding customers. If you answer \"yes\" to any of these, we'd love to chat:\n\n**Know a pharma/biotech team** working on biologics characterization?\n\n**Know an academic lab or core facility** doing protein analysis?\n\n**Know a contract research organization (CRO)** looking to scale analytical capabilities?\n\n**Resonate with our mission?** → Share this post!\n\n —_David_, _Andrew_, _Vishnu_ ([**10x Science**](https://10xscience.com/))\n\n![uploaded image](/media/?type=post&id=97760&key=user_uploads/3203252/4a578736-2de7-43de-b605-0152e507bf2b)\n\n",
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        "title": "Illume Labs - Your 24/7 personalized health companion",
        "tagline": "Illume is a 24/7 personal health companion that connects your wearables, nutrition, workouts, and lab results to give you personalized, actionable insights over text. ",
        "body": "**Hey everyone 👋!** We’re [Pari](https://www.linkedin.com/in/paridhilatawa/), [Frank](https://www.linkedin.com/in/frank-lee-/), and [Jin](https://www.linkedin.com/in/jinwong01/), co-founders of Illume. \n\n**TL;DR:** Illume is a 24/7 personal health companion that connects your wearables, nutrition, workouts, and lab results to give you personalized, actionable insights over text. \n\n**Join our private beta waitlist: [www.illumelabs.ai](http://www.illumelabs.ai). \\\nWatch Our [Launch Video](https://www.youtube.com/watch?v=bsfFOzB0Nho):**\n\n**Problem:** \n\nHealth data is more abundant than ever, but it remains scattered. Nearly six in ten U.S. adults own a wearable or connected health device, and nearly one in three used an AI chatbot for health questions. People are generating more health data than ever and actively looking for help understanding it, but their data and guidance still live in separate places. \n\nWe saw this firsthand as family members struggled to piece together glucose readings for diabetes and blood tests for cardiovascular risk, all while juggling a pile of fitness, nutrition, and health-tracking apps that never talked to each other. \n\nPeople increasingly turn to ChatGPT or Claude for answers, but without continuous context to their health, the advice is often generic, incomplete, and difficult to track over time.\n\n**Our solution:** \n\nIllume creates one home for your health and gives you a personal health companion you can text. \n\nToday, you can: \n\n* Connect your wearable and automatically sync sleep, activity, and recovery data \n* Text Illume photos of your meals to log and understand your nutrition \n* Record workouts and track your training \n* Upload bloodwork and lab results \n* Ask questions about your health using the context of your own data \n* Receive proactive insights about what changed, what it might mean, and what to do next\n\nFor example, you can ask: \n\n* “Why am I so exhausted today? Make me a dashboard of all the relevant metrics!” \n* “How has eating late affected my sleep?”\n* “Am I recovering well enough to train? Run a regression!”\n* “What should I focus on before my next blood test?”\n\nIllume connects patterns across your data that individual platforms can’t see, giving you a more complete picture of your health instead of another isolated score or dashboard. \n\nYou can text your Illume agent for quick answers or use the platform to explore your data, trends, and history in greater depth.\n\n**Our mission**\n\nOur long-term vision is to make healthcare more proactive, continuous, and personalized. \n\nBy combining continuous biometrics with nutrition, training, blood panels, and eventually genetic risk, Illume could help surface meaningful changes earlier and help people understand their susceptibility to conditions such as cardiovascular disease, diabetes, and Alzheimer’s. \n\nWe want to help people move from asking, “What happened?” to understanding what may be changing and what they can do about it. \n\n**Who’s it for:** Illume is initially designed for people who are proactive about their health and longevity- especially lifters, biohackers, and anyone already using wearables, blood panels, CGMs, nutrition trackers, or genetic testing.  \n\n**Who we are:** We’re three MIT graduates who met in freshman year! \n\n[Pari Latawa](https://www.linkedin.com/in/paridhilatawa/): MIT BS &amp; MEng in CS + Molecular Biology. Previously conducted research at the Broad Institute, Eli Lilly, &amp; MD Anderson. Founded EasyComm to build tools for children with Down Syndrome, and SparkED, a health-literacy nonprofit.\n\n[Frank Lee](https://www.linkedin.com/in/frank-lee-/): MIT BS in Computer Science and Mathematics. Conducted RLAIF research for language models at MIT CSAIL and previously built agent systems at Windsurf.\n\n[Jin Wong](https://www.linkedin.com/in/jinwong01/): MIT BS in Business Analytics. Previously conducted research at the MIT Media Lab and deployed coding agents at Windsurf. \n\n**Our ask:** \n\n**Join the private beta waitlist at [www.illumelabs.ai](http://www.illumelabs.ai).** We’ll invite an initial group of founding members to receive early access and help shape the product. \n\n**If you want to chat or can intro us** to someone in training, sports, longevity, and/or biohacking, email us at [founders@illumelabs.ai](mailto:founders@illumelabs.ai). We’d love to stop by, chat, and show you how Illume works! ",
        "url": "https://www.ycombinator.com/launches/RiI-illume-labs-your-24-7-personalized-health-companion",
        "votes": 28,
        "created_at": "2026-07-20T17:02:49.112Z"
      }
    ]
  },
  {
    "id": 31425,
    "name": "WonderTx",
    "slug": "wondertx",
    "former_names": [
      "A Company in Stealth Mode Incorporated"
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    "website": "https://wondertx.ai/",
    "all_locations": "San Francisco, CA, USA; Remote",
    "long_description": "We’re an AI-native biotech, working on replacing needles with pills. Many drugs, like insulin or Ozempic, are wonderful except you have to inject them. We discover new molecules that you can take as a pill instead. Because the pills operate on biology which has been shown to be effective in people, rather than merely in animal models of disease, we avoid what has historically been the single biggest risk in drug discovery.  This should dramatically increase our probability of success.\r\n\r\nThere’s $200B of treatable diseases and validated human biology without a pill because discovery programs lack a tractable chemical starting point. This is a zero-shot inference problem, because these targets have no training data, so LLMs and other historical interpolative AI approaches are insufficient. Instead, we are building our own extrapolative frontier models that reason beyond their training data. Most critically, we have repeatedly validated our platform on both our own and partner programs. On 4 biologically- and structurally-diverse targets where we had zero training data, we successfully picked binders that were validated experimentally, with confirmation ranging from primary screen hits to crystallographic structures.",
    "one_liner": "Extrapolative AI to unlock First-in-Class drugs",
    "team_size": 3,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Therapeutics",
    "launched_at": 1786648812,
    "tags": [
      "AI-powered Drug Discovery",
      "Generative AI",
      "Biotech"
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
      "Healthcare",
      "Therapeutics"
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    "regions": [
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      "America / Canada",
      "Remote",
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        "question": "What is your long-term vision? If you truly succeed, what will be different about the world?",
        "answer": "Imagine how drug discovery is done 80 years from now and it’s obvious that AI is going to be woven through every piece and every step.  No one believes that we will have less data, or that it will be easier to understand, or that we won’t need advanced statistical algorithms, or that the computers will be less capable.  There is an inevitability here.  But let’s not wait 80 years!  If we succeed then the path from conception to clinic for the toughest diseases, for the most difficult biological problems, becomes tractable.  Our practical outcome is simple: patients whose diseases cannot currently be drugged effectively or at all will live better longer lives. "
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    "url": "https://www.ycombinator.com/companies/wondertx",
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    "city": "San Francisco",
    "country": "US",
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    "primary_partner": {
      "name": "Pete Koomen",
      "url": "https://www.ycombinator.com/people/pete-koomen"
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    "founders": [
      {
        "user_id": 7359,
        "full_name": "Abraham Heifets",
        "title": "Founder/CEO",
        "founder_bio": "Founder and CEO of A Company in Stealth Mode. Previously, cofounded Atomwise, the first company to deploy deep learning for structure-based drug design ($220+ million in funding; 100 hires; signed $5B in total partnership value with Sanofi, Bayer, Eli Lilly, etc; ran largest AI-driven drug discovery experiment w/ 600+ researchers in 30 countries).  Even more previous: PhD @ U of Toronto; inventor on 15 patents + applications; IBM Research; AI for Cornell’s world champion robotic soccer team.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/abrahamheifets/",
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      {
        "id": 111814,
        "title": "WonderTx: Extrapolative AI to Unlock First-in-Class Drugs",
        "tagline": "Reindustrializing our life science ecosystem.",
        "body": "Our objective is to deliver first-in-class medicines against disease targets that cannot currently be drugged effectively or at all. Therefore, we are building a lights-out wonder drug factory, where AI runs the entire design-make-test loop. Linking superhuman judgment to robotic throughput is the path to reindustrializing our life science ecosystem, not through throwing human labor at the problem.\n\nOur team has been working on this for over a decade, so we know that there has never been a better time to build this system.  Abe co-founded Atomwise, one of the first AI-for-drug-discovery companies.  We raised $220 million; hired 100 people; signed over $5B in total deal value with big pharma companies like Sanofi, Bayer, Eli Lilly, and others; and ran the biggest application of AI to hit discovery in history. Saulo solved the synthesizability problem in generative chemistry AI and wrote structural biology software that remains the standard toolkit (KVFinder; qFit3, STCRDab). Cam co-founded Xbox &amp; Xbox Live (Microsoft) and has been a CEO, CTO, COO, and CPO across AI, biotech, healthcare, and consumer tech. \n\nToday, we see the convergence of critical enabling computational, physical, and economic trend lines. Agentic systems have become proficient orchestrators and tool integrators, as the frontier labs continue to extend the time horizons over which agents can operate; in our own hands, we have already compressed compound selection timelines 10x by replacing manual processes by agentic-run. The algorithmically-accessible chemical space has been growing exponentially for two decades, and the rise of cloud labs enables closing and scaling the design loop. Finally, the looming Big Pharma patent cliff means that the ecosystem is aligned on the value of innovative new assets. \n\nAt WonderTx, we’re beginning by replacing needles with pills. Many drugs, like insulin or Ozempic, are great except you have to inject them. We discover new molecules that you can take as a pill instead. Because the pills operate on biology that has been shown to be effective in people, rather than merely in animal models of disease, we avoid what has historically been the single biggest risk in drug discovery.  This should dramatically increase our probability of success.\n\nThere’s $200B of treatable diseases and validated human biology without a pill because discovery programs lack a tractable chemical starting point, which makes this a zero-shot inference problem.  Because these targets have no training data, off-the-shelf LLMs and other historical interpolative AI approaches are insufficient. Instead, we are building our own extrapolative frontier models that reason beyond their training data. Most critically, we have repeatedly validated our platform on both our own and partner programs. On 4 biologically- and structurally-diverse targets where we had zero training data, we successfully picked binders that were validated experimentally, with confirmation ranging from primary screen hits to crystallographic structures. ",
        "url": "https://www.ycombinator.com/launches/T5S-wondertx-extrapolative-ai-to-unlock-first-in-class-drugs",
        "votes": 13,
        "created_at": "2026-08-24T12:30:00.531Z"
      }
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    "id": 31441,
    "name": "Infera",
    "slug": "infera",
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    "website": "https://www.infera.bio",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Describe an experiment in plain English, and Infera turns it into a validated, instrument-ready run across the equipment your lab already uses. Infera handles the protocol logic, vendor-specific scripts, data, inventory, and institutional knowledge in one system. An AI-native compiler for the lab: one system from intent to execution.",
    "one_liner": "Control lab instruments with natural language.",
    "team_size": 2,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Industrial Bio",
    "launched_at": 1779916747,
    "tags": [
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      "B2B",
      "Biotech",
      "Automation",
      "AI"
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
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      "Industrial Bio"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
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    "url": "https://www.ycombinator.com/companies/infera",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/infera.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/infera-bio/",
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    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 3013936,
        "full_name": "Troy Zhang",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Infera Biosystems. Prev. experience in Nobel Prize winning lab. Dual BS in Political Science and Computation and Neural Systems from Caltech.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/troyzh",
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      {
        "user_id": 3288797,
        "full_name": "Chloe Sow",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder of Infera (YC P26). Built research software and medical devices at Harvard Medical School, Brigham and Women's, Fred Hutch and PNNL. MechE + CS at Harvard.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/chloe-sow",
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      {
        "id": 102417,
        "title": "Infera – Control Lab Instruments with Natural Language",
        "tagline": "Infera turns a plain-English experiment into a validated, instrument-ready run",
        "body": "### **⚡TL;DR**\n\n**Infera is the Claude Code for scientific instruments.** Describe an experiment in plain English, and Infera turns it into a validated, instrument-ready run, handling the protocol logic, vendor scripts, data, and inventory across the instruments your lab already uses. One system, from intent to execution.\n\n### 🤔 **The Problem**\n\nLabs run on hardware from the future and software from the past.\n\nThe instruments are extraordinary. Mass specs identify thousands of proteins, flow cytometers read tens of thousands of cells a second, and cryo-EMs image at near-atomic resolution. These are the machines behind biomarker discovery, immunotherapy, structural biology, and drug development. But the software around them still looks like it shipped in 2003, and runs like it.\n\nWe've watched labs running \\~6 instruments from 3+ vendors write a one-off script for every experiment, stitch outputs together by hand, and track inventory in a Google Sheet someone probably forgot to update last Tuesday. Scientists spend their week being human glue between machines that should already talk to each other.\n\n### **🔍How Infera Works**\n\n[**Demo Video**](https://www.youtube.com/watch?v=3GPze4X7AeA)\n\n**I. Describe the experiment in plain English.** Infera asks the right questions, surfaces edge cases, and checks against your lab’s inventory and instrument state.\n\n**II. Infera turns it into a run, accommodating both manual and automatable steps.** For programmable instruments, it generates vendor-specific scripts, executes, pulls the data back, and runs the analysis. For manual work like pipetting, gels, hand fermentation, etc., it's the context layer underneath: which step you're on, which reagent goes where, what could go wrong, and how others in the lab have run it before.\n\n**III. The knowledge stays.** Every protocol, every validated run, every edge case becomes part of the system and checked against instrument constraints, lab SOPs, inventory, and prior experiments.\n\nThat last part is the hard part. It's what general-purpose LLMs can't do, and it's what enables a researcher to trust the output the way they'd trust a trained technician.\n\n### 👩‍🔬 **Team**\n\nHi! We’re Chloe and Troy, and we're building Infera to close the gap between science and instrumentation.\n\n* Chloe studied Mechanical Engineering at Harvard. She's been building research software and medical devices since before college (Regeneron STS Scholar), including a microfluidic device that detects harmful chemicals in household products, and has been doing research at places like Fred Hutch, Brigham and Women's, Harvard Medical School, and Pacific Northwest National Lab.\n* Troy studied Computation &amp; Neural Systems at Caltech, where he held an endowed research fellowship in a top research lab as an undergraduate. He's published two first-author cancer papers, holds a first-inventor agtech patent, and built and sold two companies before Infera.\n\nBetween us, we've run experiments by hand, written the scripts, and watched the whole system break when one person leaves. That's the problem we're building Infera to fix.\n\n### **🎯Ask**\n\nWe're currently running pilots with Boston-area academic labs and cores. If any of these are you, reach out at [**hello@infera.bio**](mailto:hello@infera.bio):\n\n* **Academic labs and cores:** know a PI or core director in proteomics, genomics, flow, automation, or screening? We'd love an intro, and we're happy to set up a pilot\n* **YC founders with wet labs (diagnostics, therapeutics, synbio, tools):** we’d love to meet whoever runs your lab ops\n* **Biotech and life sciences folks:** come say hi\n* **Instrument manufacturers** whose customers wish the software were better, let's talk\n\n![uploaded image](/media/?type=post&id=102417&key=user_uploads/3288797/b9d81458-f74b-47a8-88ac-e3cf756951b9)\n\n",
        "url": "https://www.ycombinator.com/launches/Qdt-infera-control-lab-instruments-with-natural-language",
        "votes": 153,
        "created_at": "2026-06-02T15:49:42.214Z"
      }
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  },
  {
    "id": 31549,
    "name": "Enjamb Labs",
    "slug": "enjamb-labs",
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      "Enjamb Labs Inc",
      "Enjamb"
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    "website": "https://www.enjamb.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We've worked with 130 life science companies, preclinical through phase 3, to deploy AI securely inside the systems they already work in.\r\n\r\nPharma runs on decades of closed software like Benchling, Veeva, Medidata, and SAS. None of it has an AI layer or speaks MCP, so agents cannot reach the systems where the work lives.\r\n\r\nEnjamb is that layer. Our agents run on top of existing systems and data, with no migration, and they reach the lab systems and internal tools no other AI touches. They learn how a team works, then do the work in Slack, in Teams, and in the systems themselves, across preclinical research, clinical evidence, trial design, statistical programming, and regulatory submissions.\r\n\r\nThis is AI tailored for how medicine gets made.",
    "one_liner": "The AI Workforce for Biopharma",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B",
    "launched_at": 1779929226,
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    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/enjamb/",
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      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
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      {
        "user_id": 3164441,
        "full_name": "Rayan Mubarak",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder of Enjamb. I'm an ML and biology researcher. At 17, I published a first-author cancer ML paper in JMIR that beat SOTA models by 20%. Previously, I researched drug therapeutics for pesticide poisoning (30% more effective) and presented at top bio conferences (SOT, ISCB).",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/rayanmubarak/",
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      {
        "user_id": 3208056,
        "full_name": "Maadhav Deekshitha",
        "title": "Founder/CTO",
        "founder_bio": "Maadhav is an AI researcher. He was the youngest engineer at Dell's AI Lab, shipping AI CPU optimization on supercomputing infra,. He also worked at Broadcom R&D developing SOTA AI ASICs. For fun, he built a Rocket League AI trained on 1M+ sessions that beats the top 1% of players.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/maadhav-deekshitha-0921b4243/",
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      {
        "id": 102105,
        "title": "Enjamb - The AI Workforce for Biopharma",
        "tagline": "Enjamb runs the entire drug program for biopharma teams, from discovery to FDA approval.",
        "body": "Hey YC 👋\\\n\\\nWe’re Rayan and Maadhav, and today we’re excited to launch [Enjamb Labs](https://www.enjamb.ai/).\n\n<https://youtu.be/5DwdtiLvViM>\\\n\\\n**TL;DR**\n\nEnjamb runs the entire drug program for biopharma teams, from discovery to FDA approval. \\\n\\\nPharma runs on decades of closed legacy systems like Benchling, Veeva, Medidata, and SAS, with no AI layer and no MCP to connect agents to them. So we built that connection layer. \\\n\\\nOur agents run on top of their existing systems and data, with no migration, and do the work across the whole program: clinical evidence, trial design, statistical programming, and regulatory submissions.\\\n\\\nIn just 3 weeks since launch, we're already being used by 500+ employees at top pharma companies, including Johnson &amp; Johnson, AbbVie, Bristol Myers Squibb, Merck &amp; Sanofi.\n\n**The Problem: Pharma’s systems have no AI layer**\n\nIt costs $2.6 billion and 10+ years to ship a single drug, and most of that isn't the science. It's the work around it, spread across decades of closed legacy systems that don't talk to each other:\n\n* Benchling stores the data\n* Veeva holds the documents\n* Medidata runs the trials\n* SAS does the stats\n\nAgents are **_finally_**  good enough to do real scientific work. But there's no AI layer on these systems, and no MCP to plug an agent into where pharma's data and work actually live.\n\n**So, we built that connection layer we wished existed.**\\\n\\\n**What We Offer: Agents that run on top of pharma’s existing systems**\n\nEnjamb invented the fastest way to run a drug program: a single agentic workspace that unifies the evidence, analysis, and regulatory work.\n\nOur agents:\n\n* Synthesize up-to-date clinical evidence from the literature and real-world data to pinpoint the right endpoints and patient populations\n* Design trials and run the statistical programming behind them, including SDTM, ADaM, TFLs, and QC\n* Draft and audit regulatory documents like INDs, protocols, SAPs, and NDAs\n\n— all in one auditable workspace, with the context carried end to end.\\\n\\\n**The results:** Enjamb generates an FDA submission package in **48 hours** instead of 8 months, with **23x fewer errors** than GPT-5.5. And **98%** of the datasets it produces pass Pinnacle 21, the FDA's standard, on the first pass, before a human even reviews.\\\n\\\n**And it all runs in the browser**: full Word, Excel, and PowerPoint editors, a Python and R compute environment, 100+ scientific integrations, and 50+ fine-tuned models for in-silico validation. No setup.\n\n\\\n**Our Story: Two Researchers Who Lived the Problem**\n\nWe're **Rayan** (ML/Bio researcher, first-author paper @ JMIR, presented @ SOT and ISCB, beat SOTA by 20%) and **Maadhav** (ex-Dell AI Lab, ex-Broadcom R&amp;D).\\\n\\\nWe kept hitting the same wall in research: the science was the easy part. Everything around it, the evidence, the analysis, the documents, was fragmented, manual, and disconnected, and the context died at every handoff.\\\n\\\nSo we built what we always wanted: one workspace where agents carry the context across the entire drug program.\n\n**If no one's going to build it right, we will.**\\\n\\\n**Get Started**\n\n* See Enjamb at [enjamb.ai](http://enjamb.ai)\n* Or reach us at [rayan@enjamb.ai](mailto:rayan@enjamb.ai) for a demo\n\n![uploaded image](/media/?type=post&id=102105&key=user_uploads/3164441/9839d8af-27a3-4fd0-b6af-c82405bc817f)\n\n",
        "url": "https://www.ycombinator.com/launches/QYr-enjamb-the-ai-workforce-for-biopharma",
        "votes": 20,
        "created_at": "2026-05-28T18:48:05.818Z"
      }
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    "id": 32170,
    "name": "Gutgutgoose",
    "slug": "gutgutgoose",
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    "website": "https://gutgutgoose.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Most data companies pay for their data. Ours pays for itself.\r\nCustomers send us a stool sample. We sequence it, build a metabolic model of that gut, and simulate whether each probiotic strain has an open niche it can colonize. We formulate the best candidates, then re-sequence 90 days later to see what actually took hold and what changed. Each cycle gives us the baseline gut, the intervention, and the outcome. At scale, the endgame is models that predict chronic disease years before symptoms.",
    "one_liner": "We sequence your gut and build the probiotics it’s missing",
    "team_size": 7,
    "industry": "Healthcare",
    "subindustry": "Healthcare",
    "launched_at": 1785190964,
    "tags": [
      "Consumer",
      "Biotech",
      "Personalization",
      "Health & Wellness"
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    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
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    "regions": [
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      "America / Canada"
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    "stage": "Early",
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    "demo_day_video_public": false,
    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/gutgutgoose",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/gutgutgoose.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/gutgutgoose/",
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    "primary_partner": {
      "name": "Tyler Bosmeny",
      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
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    "founders": [
      {
        "user_id": 991771,
        "full_name": "Leon Mojarrabi",
        "title": "Founder",
        "founder_bio": "started university at 14, spent four years at a yc tech unicorn, then launched a pharmacy that hit $4M in revenue to fund GutGutGoose; strain-level metabolic modelling of the gut to catch disease decades early. I'm unreasonably interested in your poop's data.",
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        "linkedin_url": "https://www.linkedin.com/in/leon-moj",
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      {
        "user_id": 3677890,
        "full_name": "Anis Mihrshahi",
        "title": "Founder",
        "founder_bio": "Youngest Math Olympiad champion in state's history. \n\nAt GutGutGoose, I work operations behind the scenes and co-built the mechanistic algorithm behind our personalised probiotics.\n\nFun Facts: World Catan #1, Australian Powerlifting Champion, State Discus Champion, organised national conferences that brought 2000+ Australians in to think about creating meaningful change in society.\n\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/anis-mihrshahi-2a342a336/",
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    "jobs": [],
    "news": [
      {
        "title": "Meet The YC Startups Betting On What Comes Next",
        "url": "https://www.forbes.com/sites/dariashunina/2026/09/03/meet-the-yc-startups-betting-on-what-comes-next/",
        "date": "Sep 03, 2026"
      }
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    "launches": [
      {
        "id": 108277,
        "title": "Gutgutgoose: Personalized probiotics",
        "tagline": "GutGutGoose builds personalized probiotics guaranteed to colonize, then uses every retest to train models of the human gut.",
        "body": "# TL;DR: We’ve created the last probiotic you’ll ever need - personalised, constantly improving, while also generating valuable data.\n\n### \n\n# What we do and why\n\nYour gut is the most unique part of your body, different enough that the same probiotic can work for one person and do nothing for another. Most probiotics disappoint because they’re one formula for everyone, and in any gut most of their strains never establish. GutGutGoose makes a probiotic that won’t work for any gut but yours: we sequence your microbiome, find which strains can establish in the microbes you already have, compound those into your formula, and re-sequence you weeks later to confirm they took hold.\n\nIt’s for people who’ve tried things for IBS, bloating, digestion, mood, weight or GLP-1 symptoms without luck. If it doesn’t work you get your money back, and retesting shows how your gut changed, so you know it’s not a placebo.\\\n\\\n<https://youtu.be/Cn6oGduZosc>\n\nThe innovation is accurately predicting which bacteria will live in a gut, which is extremely hard. It’s built on two peer-reviewed models, MICOM and AGORA2, and more than 7,000 metabolic reconstructions of gut bacteria, and it finds the strains that can hold in your gut’s community. On two published human cohorts, its predictions matched what actually established 84.7% and 76.7% of the time, and we reproduced the first at 78.1% ourselves. Off-the-shelf products manage under 5%. Once we can control which bacteria live where, we can run precision interventions in the gut and engineer it.\n\nWe’re a supplement today and can’t treat disease, but each cycle produces a record of one gut, one intervention, and the result. That record makes each customer’s next formula better, and across thousands of people it becomes the first longitudinal dataset of the human gut responding to known, controlled changes. Our vision is to use changes in metabolic pathways over time to predict Alzheimer’s, dementia and Parkinson’s a decade before symptoms appear.\n\n![uploaded image](/media/?type=post&id=108277&key=user_uploads/991771/50caffe3-e0c9-44e5-9212-da1bbabc0e00)\n\n# Traction\n\nWe gave a talk at an endometriosis event, and 20 of the 50 people joined our waitlist. The first night we opened paid deposits, we called five people from that list at 7:30pm. Three picked up, and we managed to get seven sign ups, they were so excited they referred and paid for their families that night. Five of the 20 referred someone else, and our traction has only grown since.\n\n# Team\n\n![uploaded image](/media/?type=post&id=108277&key=user_uploads/991771/13686614-1a12-4b14-a657-d33d042b3ee6)\n\nThe GutGutGoose founders winning the YC product pitch\n\nLeon started university at 14 and spent four years at a $3bn YC unicorn. After we started GutGutGoose, he built a pharmacy, now doing $4M a year, to fund the science behind our engine. Anis joined in September; he co-built the model, runs operations, and is the youngest ever to win Queensland’s Math Olympiad. He’s on a Chancellor’s Excellence Scholarship in AI and Data Science, and (fun fact) was once the world’s number one Catan player.\n\n# The ask\n\nWe’d love introductions to gut-health clinics, patient communities, or microbiome researchers who could help us recruit and shape the 80-person study. Email us at [admin@gutgutgoose.com](mailto:admin@gutgutgoose.com). More at [gutgutgoose.com](http://gutgutgoose.com).",
        "url": "https://www.ycombinator.com/launches/SAP-gutgutgoose-personalized-probiotics",
        "votes": 145,
        "created_at": "2026-07-30T15:49:21.242Z"
      }
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    "id": 32257,
    "name": "Spectre Intelligence",
    "slug": "spectre-intelligence",
    "former_names": [
      "Spectre Capital"
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    "website": "https://www.spectreintelligence.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Spectre Intelligence is a neotrading firm. We create AI traders instead of hiring human ones. Currently trading semis + bio.",
    "one_liner": "We are a multi-strategy trading firm that trains AI traders. ",
    "team_size": 2,
    "industry": "Fintech",
    "subindustry": "Fintech -> Asset Management",
    "launched_at": 1787421048,
    "tags": [
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      "Investing",
      "Trading",
      "Semiconductors"
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
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    "regions": [
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      "America / Canada"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/spectre-intelligence",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/spectre-intelligence.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://linkedin.com/company/spectre-intelligence-ai",
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      "name": "Ankit Gupta",
      "url": "https://www.ycombinator.com/people/ankit-gupta"
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    "founders": [
      {
        "user_id": 1251785,
        "full_name": "Michelle Li",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder and CEO at Spectre Intelligence, on leave from Harvard (I studied Economics & Biology and was originally class of '27). Previous research across generative protein language models, bacteriophages, CRISPR Cas9, and more. ISEF grand award winner, RSI and STS scholar.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/michelle-zhiya-li/",
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      {
        "user_id": 2574740,
        "full_name": "Joshua Zyzak",
        "title": "Founder/CTO",
        "founder_bio": "Founder at Spectre Capital, CS and EE at Harvard, researched deep-RL controllers for quadruped robots and multimodal LLM-robotics",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/josh-zyzak/",
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      {
        "id": 112001,
        "title": "Spectre Intelligence - a neotrading firm",
        "tagline": "We're an AI trading research firm, currently in semiconductors and biotech.",
        "body": "TL;DR: Spectre Capital is a multi-strategy trading firm run by AI agents, not human analysts. We deploy real capital to capture every decision, reasoning trace, and ground-truth outcome, so our agents learn and compound from every mistake. Most AI trading labs pay egregious amounts for training data. Ours pays us when we win.\n\nWe left Harvard after starting a hedge fund from our college dorm (go Currier!).\n\nNow we’re building Spectre Intelligence, a neotrading firm training superhuman AI traders. \n\n**The problem**\n\nIntelligence is getting cheaper, and it will rewire trading. But the scarcest input to markets isn't compute or data feeds or latency, but rather judgment. That data is fragmented, scarce, and the foundation of entire careers. It is also entirely proprietary and guarded fiercely by firms.\n\n**What we do**\n\nWe collect judgment by trading live. Every position our agents take generates decisions, full reasoning traces, and ground-truth P&amp;L. We think this is the ultimate alignment of incentives in one of the largest TAMs in the world.\n\nThe structure is evolutionary from first principles: open competition inside each pod, and strategies and pods are born and killed constantly. \n\nUnlike a traditional pod shop, spinning up a new pod costs compute, not headcount. Our agents work superhuman hours, monitor a superhuman number of things, and learn from every real and simulated trade. Our AI traders develop specialized strategies in parallel, allowing us to scale far beyond the limits of human bandwidth. This is an edge that scales, compounds, and isn't held by any one employee.\n\n**Traction**\n\n\\- Two live pods: semiconductors and biotech equities. (We picked the verticals, not our AI traders.)\n\n**Why now?**\n\nAI is the worst it will ever be, LLM traders aren't even good yet, but we believe the writing is on the wall and want to be on the frontier. Quant funds were arguably the first agent labs: ML theory embedded for decades, quants as very expensive agents. We're building the generation after.\n\n**The ask**\n\nIf any of this resonates, feel free to reach out: michelle@spectreintelligence.ai\n\nWe’re also collecting an expert network across semis + biotech, so if you have experience, feel free to join at <https://www.spectreintelligence.ai/experts>",
        "url": "https://www.ycombinator.com/launches/T8T-spectre-intelligence-a-neotrading-firm",
        "votes": 5,
        "created_at": "2026-08-24T15:47:00.614Z"
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    "id": 32261,
    "name": "Rasyn",
    "slug": "rasyn",
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ffda7ce34aee49cfec95fa8aef9ed1a70e4c3e28.png",
    "website": "https://www.rasyn.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We build foundational AI models for chemistry. Almost nothing you buy is a single chemical. Products like datacenter coolant, GPU thermal paste, paint, and glue are formulations, which are mixtures of around ten ingredients each, and demand for them has grown quickly because of AI and EVs.\r\n\r\nDesigning a new formulation is essentially a search problem, because there are more possible mixtures than anyone could ever test, and none of them can be predicted in advance. As a result, bringing a single formulation to market today takes three to five years of trial and error in a lab.\r\n\r\nOur models predict how a mixture will behave far faster than the standard methods used today, which lets us design new formulations in weeks rather than years, at a fraction of the cost. To prove this, we used them to invent three chemicals that never existed before, including a PFAS-free coolant for datacenters, and we have since synthesized and patented all three.\r\n\r\nWe are already working with some of the largest manufacturers to design formulations for them, and we are building our own lab to generate the data that will train our next models.",
    "one_liner": "Making chemicals 100,000x faster",
    "team_size": 3,
    "industry": "Healthcare",
    "subindustry": "Healthcare",
    "launched_at": 1785736030,
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    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/rasyn-ycs26/",
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      "name": "Ankit Gupta",
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      {
        "user_id": 3475945,
        "full_name": "Ansh Tiwari",
        "title": "Founder/CEO",
        "founder_bio": "Building AI for scientific discovery | Physics @ Caltech, ex-NASA, Author of 3x ICML papers | Masason Foundation Member",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ansschh/",
        "x_url": "https://x.com/4n5che",
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      {
        "user_id": 972683,
        "full_name": "Ayush Chauhan",
        "title": "Founder/CTO",
        "founder_bio": "AI for chemical discovery | ex-Speechify (50M+ users) & 2x YC engineer | NeurIPS-published ML research, used by OpenAI & Meta",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ayushchauhan0203/",
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      {
        "user_id": 3481804,
        "full_name": "Daood Hashmi",
        "title": "Founder",
        "founder_bio": "Researching in the Arnold and Marcus Lab at Caltech",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/daood-hashmi/",
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      {
        "id": 108802,
        "title": "Rasyn: One place to run any Chemistry task",
        "tagline": "Towards a General Intelligence for Chemistry",
        "body": "**Hey!**\n\nWe're **Rasyn**, and we built a platform called **Marigold**. Give it any chemistry task in plain language and it pulls the literature, reads your files, picks the tools, sets up and runs the calculations on its own machine, and hands back the numbers along with its reasoning what it did to get them. \n\nIt's live at [app.rasyn.ai](http://app.rasyn.ai); here's a short demo: <https://youtu.be/ru1BeRBlRec>\n\n\\\n**Here's the team:** \\\nOur team has worked at NASA JPL, and researched with scientists from Nvidia and Deepmind. Our team has worked on softwares serving 50 million users, and their research was used by frontier AI labs including OpenAI and Meta. Our team studies Computational Chemistry at Caltech, has done Total Synthesis for 4+ years, and researched alongside Nobel Laureates, so they have faced these problems firsthand. They'd lose hours deciding which tool to call next, moving outputs between disconnected tools, and running a single program at a time.\n\n**Problem:** \\\nChemistry has countless software. There's one for predicting a structure, another for proposing a route, another for reading a spectrum. None of them talk to each other, so you're stuck feeding outputs between them; this clerical work is where the time goes, none of which is science.\n\n\\*\\*Solution:\\*\\*\\\nWe tried coding agents first. They're built to write software, so a chemistry task gets handled as a scripting problem and the chemistry goes unverified. Marigold checks between steps and stops when something doesn't hold.\n\nIt runs on its own machine, so it installs and executes what it needs. It works with the tools chemists already use, submits jobs to your cluster, and runs on whatever model endpoint you have, so your data can stay in the building. In the demo we hand it four thousand compounds and ask which are worth screening. It does the whole thing itself.\n\nLonger term we're building toward a general intelligence for chemistry. Marigold today reasons over existing tools, and that's where we start.\n\n**The Ask:**\n\nThis is only the first version; it will break. And we'd like it to break in front of chemists, so if you run any sort of chemistry, please try it and tell us where it falls over. \n\nWe'd also love intros to any lab doing chemistry, computational or wet, since we want to sit down and work through a real problem with people. \n\nHappy to answer any questions!",
        "url": "https://www.ycombinator.com/launches/SIs-rasyn-one-place-to-run-any-chemistry-task",
        "votes": 9,
        "created_at": "2026-08-03T19:40:21.411Z"
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    "id": 32521,
    "name": "Molagri",
    "slug": "molagri",
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        "body": "**TLDR:** We build next-generation pesticides from engineered insect virus-like particles. They target a single pest species, stay safe for humans, pollinators, and the environment, and can be reprogrammed over time as pests evolve resistance.\n\nPesticides underpin modern agriculture and food security, accounting for $80B in annual spend. Without effective pesticides, we can't grow enough food.\n\n<https://youtu.be/6rxXDJeC14Y>\n\n**The Problem 🌱**\n\nPests are developing resistance faster than new pesticides can be invented to replace the failing ones. At the same time, regulators are banning core active ingredients over environmental and human toxicity. Farmers are losing tools from both ends at once.\n\n**Our Solution 🪲**\n\nWe build pesticides out of insect virus-like particles. We start with a virus that only infects insects and is harmless to humans. We strip out its genome so it can't infect or replicate, and we keep the empty protein shell as a delivery vehicle.\n\nWe engineer that shell with two parts:\n\n* A **targeting element** that recognizes a single pest species (or a set of species). This is what keeps the product species-specific and invisible to humans, pollinators, and beneficial insects.\n* A **payload** that kills the pest.\n\nBoth parts are swappable. Changing the targeting element will modify which pest it kills. Changing the payload will result in a new kill mechanism for the same pest, which is how we update the product when resistance appears.\n\nDesigning for true pollinator safety is hard, so we did the underlying science ourselves. We solved a cryo-EM structure of a key receptor in honey bees, which lets us deliberately engineer our targeting elements so they do not bind pollinators.\n\n![uploaded image](/media/?type=post&id=108796&key=user_uploads/2273508/62f4362a-1938-4843-ab26-c9a62c742a84)\n\n**Why Us? 🧑🏽‍🔬**\n\nWe are Zaky and Min, viral structural biology PhD candidates and lab mates at the University of Toronto, where we have worked together for the past 6 years studying how viruses interact with their hosts. We have published in Nature Microbiology and Nature Communications, and Min holds the world record for the highest-resolution structure of a coronavirus spike protein.\n\n**Traction 📈**\n\nWe have signed our first grower LOI, have more in the pipeline, and produced our first set of viruses for testing. During the batch we also solved a cryo-EM structure of a honey-bee receptor that directly informs our pollinator-safe design.\n\n**Connect with us!**\n\n* **Growers and producers**: if you are fighting resistant pests or losing tools to bans, we would love to talk about your crops and target pests. Reach us at [molagri.com](http://molagri.com) \n* **Everyone else:** intros to ag and crop-science operators, or anyone who has built regulated biological products, are always welcome.",
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        "title": "Neuromorphic - Building an embodiment-agnostic “brain” that makes robots fast to deploy",
        "tagline": "We make robots smarter, and faster to deploy. Starting with wet labs!",
        "body": "Hi everyone! We’re Cem, Ege, and Vatan, the founders of Neuromorphic 👋\\\n\\\n**TL;DR** Neuromorphic is building an embodiment-agnostic “brain” that makes robots fast to deploy, and easy to manage through natural interfaces like Slack, email, and phone. Our goal is to make deploying robots in wet labs and other physical industries as easy as onboarding a new employee.\n\n<https://www.youtube.com/watch?v=6T0BDyjakmo>\\\n\\\n**The Problem**\\\nTo make robotics mainstream, we believe two problems have to be resolved:\\\n1\\. Deployment: Robots are still too slow, expensive, and engineering-intensive to deploy and adapt to new tasks and environments.\\\n2\\. Interaction: Managing and controlling robots remains too technical.\\\n\\\n**What We’ve Built**\\\nNeuromorphic packages sensors, compute, and software into a “brain” that can be attached to any robot body. An onboard LLM orchestrator turns natural-language instructions into workflows made up of reusable robotic skills, such as navigating, picking up an object, opening a refrigerator, or inspecting an area. Individual skills can be of various types; they could be AI models we develop, like Vision-Language Models and Video Action Models, or just plain code. Sensor-based safety loops monitor every action to ensure reliable execution.\n\nWe are also giving robots emails, phone numbers, and Slack access to make human-robot interaction more intuitive than using dashboards. The robots then respond to queries and take actions using the onboard LLM.\\\n\\\n**Traction**\\\nWithin a week of our first conversation with a leading biotechnology company, we deployed our robot in their wet lab, which is capable of operating autonomously 24/7, and already completed over 1500 tasks.\\\n\\\n**The Team**\n\n![uploaded image](/media/?type=post&id=109550&key=user_uploads/3607343/b28b023b-f328-4c5d-9181-720e96e5b1a3)\n\n\\\nCem: Second-time founder and did robotics research at Cambridge University. Previously built LLM routing and training acceleration, and did precision robotics.\\\n\\\nVatan: Maintained multiple open-source robotics libraries, most notably MoveIt, one of the most popular motion-planning frameworks for robotics. Led various teams in robotics startups from Series A to Series C in Japan and Singapore. Deployed over a thousand robots.\\\n\\\nEge: Did Robotics and AI research at Bilkent University. Research fellow at Nanyang Technological University. Published research in top AI and robotics conferences. Won HackMIT in 2024.\\\n\\\nCem and Ege first met as teammates in the FIRST Robotics Competition. They reached out to Vatan for mentorship and robotics advice. We spent the next three years building robots and winning competitions together. We remained close and eventually reunited to start Neuromorphic!\\\n\\\n**Ask**\\\nWe're looking for warm intros to wet lab, biotech, and physical-industry operators. If you have a repetitive physical process and have wondered whether a robot could do it, email us at [founders@neuromorphic.vision](mailto:founders@neuromorphic.vision) or [book a call here](https://cal.com/team/neuromorphic/meet-the-founders-of-neuromorphic).",
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        "title": "Gamgee: Personalised mRNA cancer vaccines for dogs",
        "tagline": "Input the tumour. Output the vaccine. 300+ dogs on the waitlist.",
        "body": "**We believe that AI meets biology is the biggest opportunity of the 21st century.** We are using AI to build autonomous systems that read biological failure, discover how to control it, and design precise repairs. We are starting with the first computationally personalised mRNA cancer vaccine for dogs.\\\n\\\n**My dog Rosie was dying of cancer.** I designed her an mRNA vaccine. Several of her tumours shrank 50-75%. I am not a biologist - I spent 17 years in machine learning. Doing it by hand took a year and around $20,000. Gamgee is turning that process into software. Our target is a vaccine design in 48 hours and an end-to-end cost of $2,000\n\n<https://www.youtube.com/watch?v=-Jb41jTc0U0>\n\n**How it works**\n\n* We **send you a collection kit** to take to your vet or veterinary oncologist. Your vet collects the tumour and matched healthy samples, which are returned for DNA and RNA sequencing.\n* Our software finds and ranks the mutations worth targeting, and keeps the evidence behind every call.\n* **Out comes an mRNA vaccine** design ready for manufacture. Gamgee designs the vaccine and oversees and coordinates every step through to administration by the treating veterinarian.\n\n\\\n**The market**\n\nApproximately **six million dogs** are **diagnosed with cancer** every year in the United States, and cancer is the leading cause of death in adult dogs. Owners who pursue specialist treatment spend thousands on surgery, chemotherapy and radiation.\\\n\\\n\\\n**The compounding data advantage**\n\nEvery dog we treat returns sequencing, immune and outcome data for a species where datasets remain scarce. That data improves target selection, which improves the next vaccine, which produces better data. We are not just early to the problem - we are the ones accumulating the data that solves it.\n\n\\\n**Proof so far**\n\n* Full computational pipeline built and executed end-to-end as a non-biologist using AI\n* 50-75% shrinkage in the responsive tumours. \n* Microscopic analysis found substantial IBA1+ macrophage and CD3+ T-cell infiltration in Rosie’s responsive lesions, with little in the non-responsive tumour.\n* Second-generation vaccine completed and ready\n* Over a billion impressions on Rosie's story.\n* Backed by Y Combinator (S26) \n\nRosie was one patient. Our next step, already well underway, is a clinician-led trial in 30 dogs to evaluate safety, immune response and clinical outcomes.\n\n\\\n**Why dogs first, and not only dogs**\n\nMost modern dog breeds were created within the last few hundred years from small founder populations and then bred within closed gene pools. This concentrates inherited cancer risk, leaving many breeds unusually prone to particular cancers.\\\n\\\nPet dogs develop cancer naturally, not in a laboratory. Their immune systems are intact and their disease progresses on timelines that let us learn quickly. Dogs are a real market in their own right and the fastest place to get good at personalised cancer vaccines.\n\n\\\n**Our ask**\n\n* **If your dog has cancer, please do get in touch about our upcoming clinical trial.**\n* Veterinary oncologists and specialty hospitals who want to be a trial site.\n* Investors in longevity or personalised medicine - would rather meet you before the trial reads out than after.\n\nWe are building Gamgee so that no other dog waits for hope which arrives too late.\n\nReach me at [paul@gamgee.io](mailto:paul@gamgee.io) or [gamgee.io](http://gamgee.io)\\\n\\\nI love you Rosie.",
        "url": "https://www.ycombinator.com/launches/SUZ-gamgee-personalised-mrna-cancer-vaccines-for-dogs",
        "votes": 186,
        "created_at": "2026-08-11T17:00:00.187Z"
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    "id": 33540,
    "name": "Osseus",
    "slug": "osseus",
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    "website": "https://osseus.ai/",
    "all_locations": "London, England, United Kingdom",
    "long_description": "In an era of cheap intelligence, the most important application of AI will be to improving human health, globally. Osseus is building the frontier medical AI lab, starting with the clinical datasets, evaluations, and reinforcement learning environments that help AI labs train more capable, aligned, and safe models.",
    "one_liner": "The frontier data platform for aligned health and biological AI",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B",
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    "batch": "Summer 2026",
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    "regions": [
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      "Europe"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/osseus",
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    "city": "London",
    "country": "GB",
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      "url": "https://www.ycombinator.com/people/jared-friedman"
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    "founders": [
      {
        "user_id": 1409413,
        "full_name": "Anish Mariathasan",
        "title": "Founder/CEO",
        "founder_bio": "Co-Founder at Osseus (YC S26). Prev. co-founded a compliance AI startup. ML research at a Harvard-HKU joint lab on computer vision for cancer research. MEng in Biomedical Engineering from Imperial College London, dropped Oxford PhD in AI for Health. Awarded academic scholarships for both.",
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        "user_id": 1979222,
        "full_name": "Ayush Patel",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder at Osseus (YC S26). 2x intern at Apple, where he built an agentic QA system. MEng in Computer Science from Imperial College London.",
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    "id": 35883,
    "name": "Halmos Labs",
    "slug": "halmos-labs",
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    "website": "https://halmoslabs.com/",
    "all_locations": "Lund, Skåne County, Sweden",
    "long_description": "Halmos Labs builds automated biotech R&D on the principles of validity and interpretabillity. We run research, and simulations answering open questions ranging from wetlab experiment design to full cancer vaccine manufacturing.",
    "one_liner": "Maximum information per experiment, minimum experiments per conclusion",
    "team_size": 3,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Industrial Bio",
    "launched_at": 1788773241,
    "tags": [
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      "Swarm AI",
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      "Europe"
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    "stage": "Early",
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      "name": "Kulveer Taggar",
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      {
        "user_id": 3682661,
        "full_name": "Mattias Akke",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Halmos Labs. Previously ML research intern at MIT (Gómez-Bombarelli's lab) and AstraZeneca, developing frontier models for drug discovery. Published at NeurIPS on Bayesian optimization with LLMs. Research intern at Cambridge in Tuomas Knowles' group studying protein misfolding diseases. Chaired Kodsport Sverige, Sweden's largest competitive programming organization.",
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        "linkedin_url": "https://www.linkedin.com/in/mattias-akke-40a83219a",
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      },
      {
        "user_id": 3654952,
        "full_name": "Linus von Ekensteen Löfgren",
        "title": "Founder/CTO",
        "founder_bio": "Founder at Halmos Labs. Previously Desk Quant at Danske Bank, building pricing algorithms and risk models across all asset classes. Conducted LLM security research at AI Sweden and quantum computing research at Chalmers. MS Engineering Physics and BA Economics from Lund University.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/linusvonekensteen/",
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      {
        "id": 117722,
        "title": "Halmos Labs; intelligent experiment design",
        "tagline": "Halmos designs experiments for biotech teams",
        "body": "Halmos enables biotech teams to get more out of every experiment. It reads the literature, runs simulations, and selects or trains models to optimize every step — so you extract more information across discovery, process development, scale-up, and manufacturing.\\\n\\\n<https://youtu.be/u1ptzxsgzWo>\n\\\n**The problem**\\\nExperiments are slow, expensive, and most of the information they could yield is wasted. Teams run tedious trial-and-error or exhaustive screens, burning months of lab time and budget to reach conclusions better experimental design would have reached far sooner. The pharmaceutical industry alone performed **$126B of U.S. R&amp;D in 2023**.\\\n\\\n**How it works**\\\nHalmos runs unbounded research by coordinating hundreds of AI agents, checks the logical consistency of their reasoning, and revisits arguments that fail validation before presenting conclusions to your team.\\\nZero installations, no data engineering, and no need for your own statistics team.\\\n\\\n**Tested in industry:**\n\n* **Bioprocessing:** In a collaboration, Halmos matched the best performance from a company's wet-lab campaign in an average of **30% fewer evaluations** across repeated simulations. Halmos repeatedly recommended testing beyond the permitted range; when the experimentalists eventually explored it, performance improved further.\n* **Food-tech fermentation:** For a company two months into fermentation optimization, Halmos simulated the organism's metabolism and identified a nutrient variable missing from the experimental plan. The team confirmed the finding and replanned its next month of experiments.\n\n\\\n**Towards increasingly large R&amp;D tasks**\\\nWe start by designing experiments. From there we take on increasingly large R&amp;D tasks until we can run full end-to-end programs for biotech teams at a fraction of the cost of a consultant — enabling new discoveries in less well funded industries like food, rare diseases, biomaterials, and agriculture. \\\n\\\n**Team**\n\n* **Mattias Akke (CEO)** researched LLM-driven Bayesian optimization at MIT, built drug-discovery models at AstraZeneca, and conducted Alzheimer's research at Cambridge.\n* **Linus von Ekensteen Löfgren (CTO)** was a desk quant at Danske Bank, researched LLM security at AI Sweden, and wrote quantum simulation code at Chalmers. Winner of the IEEE AP-S Student Design Contest 2023.\n* **Linus Särud (Founding Engineer)** has 12 years of cybersecurity experience and led offensive security at Sciber. He earned bounties for hundreds of vulnerabilities across companies including Google, Meta, Microsoft, and PayPal.\n\n\\\n**Our ask**\\\nIf you run biotech **R&amp;D, process development, assay development, or manufacturing optimization**, we want to hear from you. **Scope an experiment with us at [halmoslabs.com](http://halmoslabs.com)** or email [**hi@halmoslabs.com**](mailto:hi@halmoslabs.com) (enterprise inquiries welcome).",
        "url": "https://www.ycombinator.com/launches/Uck-halmos-labs-intelligent-experiment-design",
        "votes": 5,
        "created_at": "2026-10-05T17:00:00.106Z"
      }
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]