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I did my PhD at Harvard (Class 2019, BBS) and spent some time in biotech VC.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/frances-anastassacos", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c451dfeaa216b655fa58e3c3fdd50b696d6f508e.jpg" } ], "jobs": [], "news": [ { "title": "Takeda turns to AI drug designer after whittling down R&D focus", "url": "https://www.fiercebiotech.com/biotech/takeda-turns-ai-drug-designer-after-whittling-down-rd-focus", "date": "Oct 14, 2025" }, { "title": "Exclusive: AstraZeneca, BMS and Takeda partner with Harvard startup using AI to make antibodies – Endpoints News", "url": "https://endpts.com/exclusive-astrazeneca-bms-and-takeda-partner-with-harvard-startup-using-ai-to-make-antibodies/", "date": "May 14, 2024" }, { "title": "Khosla Ventures and Zetta Venture Partners are closing $11 million in seed funding for Nabla Bio | List23: Latest U.S. & World News", "url": "https://list23.com/433118-khosla-ventures-and-zetta-venture-partners-are-closing-11-million-in-seed-funding-for-nabla-bio/", "date": "Dec 06, 2021" } ], "launches": [] }, { "id": 23610, "name": "PipeBio", "slug": "pipebio", "former_names": [ "Pipe|bio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b6fb87df991642ec6fcea487e6c1e54683c1319f.png", "website": "https://pipebio.com", "all_locations": "Aarhus, Denmark; Remote", "long_description": "Pipe|bio is the bioinformatics cloud for antibody / peptide screening & drug development.\r\n\r\nWe enable scientists to analyze and manage massive amounts of DNA sequencing data themselves without the need for bioinformaticians or programmers. The software is highly visual and enables you to overlay and filter information from different sources across your organization; be it assay data, sequence data or other process metadata. Insights from past results can be used to guide new experiments and they get better as you upload more and more data. 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Using groundbreaking biological and computational tools that track real clinical outcomes, the company generates data that enable earlier, more reliable decisions about disease biology and drug development. \r\n\r\nParallel Bio’s platform overcomes the core limitations of traditional animal testing, which fails to encapsulate the complexity of human biology. The company is building internal pipelines of novel disease targets and drug candidates in both cancer and autoimmune disease, while collaborating with pharmaceutical companies to evaluate therapies directly in human systems to reduce the cost and risk of clinical trials. \r\n\r\nBased in San Francisco, the company was founded in 2021 by two of the world’s leading experts in replacing animal models with human-first platforms.", "one_liner": "Using the immune system to cure disease", "team_size": 20, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1626811179, "tags": [ "AI-powered Drug Discovery", "Biotech" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/parallel-bio", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/parallel-bio.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/parallelbio", "x_url": "https://twitter.com/ParallelBio", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/parallel-bio", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/983561db384b2274b11c61cd0cad3af0a429c991.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 755142, "full_name": "Robert DiFazio", "title": "Founder", "founder_bio": "Robert is a co-founder and CEO of Parallel Bio. Previously he was Director of Strategy & Research Development at Stanford University where he oversaw a $100M scientific portfolio and ran 8 large-scale R&D programs. He also worked in global health and ran vaccine programs with GSK, the NIH, and the Danish government. He completed his postdoctoral training at Stanford, holds a PhD from the University of Pittsburgh, and a BS from the University of Illinois.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rdifazio/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4cf4d5c95563ac997d7fa24815656fad44dcce8c.jpg" }, { "user_id": 688530, "full_name": "Juliana Hilliard", "title": "Founder", "founder_bio": "Juliana is a co-founder and the CSO of Parallel Bio. She previously worked at System1 Biosciences managing R&D brain organoid drug discovery teams and has extensive experience developing mini-organ systems to better model human disease and discover new drugs. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/juliana-hilliard-774717100/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f01a0e03a69ca87a163aee04657d3a371fbdff5d.jpg" } ], "jobs": [ { "id": 95519, "title": "Head of Biology", "url": "https://www.ycombinator.com/companies/parallel-bio/jobs/TvslvYR-head-of-biology", "location": "San Francisco, CA", "type": "Full-time", "role": "Science", "role_type": "Immunology", "salary_range": "$280K - $305K", "equity_range": "", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 107157, "title": "IT Operations Lead", "url": "https://www.ycombinator.com/companies/parallel-bio/jobs/ysuN5B8-it-operations-lead", "location": "New York, NY, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$147K - $154K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 107159, "title": "Head of Platform Bioengineering & Assay Development", "url": "https://www.ycombinator.com/companies/parallel-bio/jobs/YuhnxRw-head-of-platform-bioengineering-assay-development", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Science", "role_type": "Process engineer", "salary_range": "$203K - 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Currently available approaches for creating drugs work on the principle of finding one drug-one protein 'magic bullets'. However, diseases such as cancer and autoimmunity are often the result of several dysregulated proteins across many distinct biological pathways. \r\n\r\nWe are building a computational-experimental platform to design therapeutics that can target several disease-causing proteins at once. 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In my 25+ years in the field, I carried out drug discovery projects involving small molecules and peptides. I worked in environments of startup and global large pharma companies where I led teams and departments in all aspects of early drug discovery. 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I have been lucky enough to develop my experience over the years in the fields of immunoengineering, synthetic biology and computational biology in some of the best universities in the world (Harvard, University of Leeds, KAIST, EPFL). 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Currently the global rise in severe bacterial infections and the drying pipeline of effective treatments are burdensome for healthcare and economics. Sculpted by evolution, milk, first food for the newborn, provides an exemplary solution to address the nutritive and protective challenges an infant is faced with. The anti-pathogenic molecules from milk can be developed into therapeutics geared towards all-age diseases. Our first indication is bacterial vaginosis, a bacterial dysbiosis related inflammation that results in recurrent infections, disruption of homeostasis and long-term clinical complications in women of reproductive age, including but not limited to preterm births, fertility issues and increased rates of STDs. 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He is also a co-founder of three start ups; Evolve Biosystems, BCD Bioscience, and Matrubials.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/david-a-mills-82256b4/", "x_url": "https://twitter.com/davidamills", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d471f0c9932f74f5a8f80a2cfdc96060abf832e9.jpg" } ], "jobs": [], "news": [ { "title": "Matrubials Inc. awarded a competitive NIH SBIR grant", "url": "https://matrubials.com/grants/f/matrubials-inc-awarded-a-competitive-nih-sbir-grant", "date": "May 12, 2022" }, { "title": "Matrubials Inc. wins the Sacramento Business Journal's Innovation award", "url": "https://www.bizjournals.com/sacramento/inno/stories/awards/2021/11/19/innovation-awards-matrubials.html", "date": "Dec 05, 2021" } ], "launches": [] }, { "id": 24727, "name": "Output Labs", "slug": "output-labs", "former_names": [ "Jingu Health", "Output Biosciences" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/45a8d72beb3c47955dd9a980cc0185312b3b7e63.png", "website": "https://www.outputlabs.ai/", "all_locations": "New York City, NY, USA", "long_description": "Output Labs is a frontier AI research lab. 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This allows our customers to discover, develop, manufacture and deliver cell therapies, antibodies, vaccines and other novel therapies faster, at lower expense, and with higher rates of success.\r\n\r\nOur customers include top-25 biopharma companies and seed-stage biotechs that are developing cell therapies, antibody therapeutics and vaccines (including one of the few companies with an approved CAR T cell therapy).\r\n\r\nHear from the team at https://www.youtube.com/watch?v=IE9x_tm0XnI", "one_liner": "The dynamic single-cell analysis company", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1629064445, "tags": [ "AI-powered Drug Discovery", "Machine Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/cellchorus", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/cellchorus.json", "year_founded": 2020, "city": "Houston", "country": "US", "linkedin_url": "https://www.linkedin.com/company/cellchorus", "x_url": "https://twitter.com/CellChorus", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/cellchorus", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b81eadcf3657043bd35cb166adc3ac4e9ad367e8.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/60f4cb531013b5a90d6ebe5b9fa2752fd477993f.png" ], "primary_partner": null, "founders": [ { "user_id": 728099, "full_name": "Dan Meyer", "title": "Founder", "founder_bio": "Daniel Meyer is the CEO of CellChorus, the leader in providing dynamic single-cell functional analysis to the life sciences industry. \n\nDan was the Chief Operating Officer and a board member of Genospace until its acquisition by HCA Healthcare (NYSE:HCA). He was a member of the venture capital investment teams at Arboretum Ventures and PJC.vc.\n\nDan holds a B.A. in mathematics and economics from Middlebury College and an M.B.A. from the Tuck School of Business at Dartmouth.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/danmeyer/", "x_url": "https://twitter.com/_danmeyer", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5ef7eb92417f631ec599fb13d1969f305bdc2265.jpg" }, { "user_id": 740003, "full_name": "Navin Varadarajan", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/navin-varadarajan-95207814", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/58efe6425a789bb9ede6fcb583e26de340019008.jpg" } ], "jobs": [], "news": [ { "title": "NCI Award CellChorus and Stanford Medicine SBIR award — CellChorus", "url": "https://cellchorus.com/news/nci-award-cellchorus-and-stanford-medicine-sbir-award", "date": "May 11, 2026" }, { "title": "BioTools Innovator VANGUARD awards CellChorus contract to support pathogen-immune profiling", "url": "https://cellchorus.com/news/biotools-innovator-vanguard-awards-cellchorus-contract-to-support-pathogen-immune-profiling", "date": "Aug 25, 2025" }, { "title": "Four abstracts at SITC 2024 to feature CellChorus TIMING data", "url": "https://cellchorus.com/news/four-abstracts-at-sitc-2024-to-feature-timing-data", "date": "Nov 04, 2024" }, { "title": "Cancer Discovery publication applies TIMING to show increased serial killing by novel CAR NK therapy — CellChorus", "url": "https://cellchorus.com/news/cancer-discovery-publication-applies-timing-to-show-increased-serial-killing-by-novel-car-nk-therapy", "date": "Oct 23, 2024" }, { "title": "University of Houston-founded company secures $2.5M in NIH grant funding - InnovationMap", "url": "https://houston.innovationmap.com/university-of-houston-cellchorus-nih-grant-2669359073.html", "date": "Oct 08, 2024" } ], "launches": [] }, { "id": 24795, "name": "SFA Therapeutics", "slug": "sfa-therapeutics-inc", "former_names": [ "SFA Therapeutics, Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5873834b208e59808a81e6f7b65403be4dd9fc78.png", "website": "https://sfatherapeutics.com/", "all_locations": "Philadelphia, PA, USA", "long_description": "SFA Therapeutics is creating therapeutically-tailored oral drugs that regulate the immune system; with a focus on autoimmune diseases and cancers that originate from chronic inflammation. There are over 300 million patients who suffer from autoimmune diseases. Our treatment is designed to block the root cause of many of these chronic inflammatory diseases, with safe oral drugs. We have strong clinical data from a phase 1b clinical trial in psoriasis, and human data in pancreatic cancer, uveitis and NASH. we have a total of 9 drugs in our pipeline and 15 patents.", "one_liner": "Regulating the immune system to block cancer & autoimmune diseases", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare -> Therapeutics", "launched_at": 1624038758, "tags": [ "AI-powered Drug Discovery", "Anti-Aging", "Biotech", "Health & Wellness", "Biotechnology" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Healthcare", "Therapeutics" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": [ { "question": "What is your company going to make? 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This platform has led to the development of six drugs to treat immuno-inflammatory diseases, using psoriasis as a proof-of concept for autoimmune diseases and chronic liver diseases that are caused by inflammation, including Hepatitis B virus-mediated chronic liver disease, NASH and Hepatocellular Carcinoma (HCC), the most common form of liver cancer. SFA’s two most advanced assets are SFA001 and SFA002, for liver disease and autoimmune diseases, respectively. SFA002 has an approved IND and is ready to enter into clinical trials in patients.\r\n\r\nOur plan is to develop these drugs through phase 2 and create phase 3-ready assets, to license to pharma and biotech partners, in exchange for license fees, royalties and milestone payments." } ], "question_answers": [ { "question": "What is the core problem you are solving? Why is this a big problem? 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An experienced drug developer with over 30 years of experience, he helped develop 34 drugs and 12 medical devices, including Enbrel, Effexor, Protonix, Prevenar, Ozurdex, Botox for Overactive Bladder, Chronic Migraine and Cerebral Palsy. He holds BS degrees in Physics and EE from Washington University, an MBA from Drexel, and a PhD from Rutgers.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ira-spector-00414310/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8d17c573b2d929f75bf6ca2e9f4e2cb586e95cd7.jpg" }, { "user_id": 475228, "full_name": "Alla Arzumanyan", "title": "Founder", "founder_bio": "MS is Microbiology,\nPhD in Biotechnology,\nAssistant Professor at Temple University,\nCo-Founder and Co-Inventor at SFA Therapeutics, Inc.\nPerformed a case-control, Proof-of-Activity Human study to evaluate treatment effect of SFA002 drug (proprietary of SFA Therapeutics, Inc.) on patients with plaque psoriasis.\nWorked with Axella Research on pre-IND application for SFA002.\nWorked with HEC (Sunshine Lake Pharma Co., China), Theravectys, and ThirdLaw LLC (still collaborating with the latter)", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alla-arzumanyan-66a79a18b", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c53d726559d2b7621f20dc5e8b2195b813db97fc.jpg" }, { "user_id": 475711, "full_name": "Mark Feitelson", "title": "Founder", "founder_bio": "Mark Feitelson received his Ph.D. in Microbiology/Immunology from UCLA. He began his work with hepatitis B at Stanford University, and was then recruited to the Fox Chase Cancer Center by Dr. Baruch Blumberg (Nobel laureate). In 1991, Dr. Feitelson moved to Thomas Jefferson University, and in 2007, to Temple University as a Professor of Biology. His work has been supported by NIH, industry and foundations. He has more than 150 publications and 180 abstracts. He is also CSO of SFA Therapeutics. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mark-feitelson-707771a", "x_url": "https://twitter.com/feitelson", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3192ca2979c1fea02c4df51becafcb0e76d2b53d.jpg" } ], "jobs": [], "news": [ { "title": "SFA Therapeutics Announces Updated Psoriasis Data", "url": "https://www.globenewswire.com/news-release/2023/11/09/2777360/0/en/SFA-Therapeutics-Announces-Updated-Psoriasis-Data-for-SFA-002-Bridge-Financing-and-Foundational-Patent.html", "date": "Nov 09, 2023" }, { "title": "SFA Therapeutics announces results for SFA-002 from Phase", "url": "https://www.globenewswire.com/en/news-release/2023/08/14/2724489/0/en/SFA-Therapeutics-announces-results-for-SFA-002-from-Phase-1b-Cohort-1-clinical-trial-for-treatment-of-mild-to-moderate-psoriasis.html", "date": "Aug 14, 2023" }, { "title": "Short-chain fatty acids in cancer pathogenesis | Cancer and Metastasis Reviews", "url": "https://link.springer.com/article/10.1007/s10555-023-10117-y#Fun", "date": "Jul 11, 2023" }, { "title": "Treat Disease, Not the Symptoms", "url": "https://www.appliedclinicaltrialsonline.com/view/treat-disease-not-the-symptoms", "date": "Mar 07, 2023" }, { "title": "A biotech CEO’s favorite ‘F’ word At SFA Therapeutics, patients are the “focus” ", "url": "https://sfatherapeutics.com/sfa-therapeutics-ceo-ira-spector-profiled-in-pharmavoice/", "date": "Jan 26, 2023" } ], "launches": [] }, { "id": 25151, "name": "Keylika", "slug": "keylika", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd12fc4dc5d053441f31a9c528579e0d16f3449e.png", "website": "https://www.keylika.com", "all_locations": "San Francisco, CA, USA; Walnut Creek, CA, USA", "long_description": "Keylika is an early stage biopharma startup developing metal-based drugs using a platform synthesis technology to treat unmet medical needs. We then administer these drugs using proven delivery technologies to effect the best clinical outcomes. \r\n\r\nOur first product is a small molecule iron drug (new molecular entity) delivered by a wearable skin patch to treat Iron Deficiency Anemia (IDA), with significantly higher safety, efficacy and tolerability over existing treatments. This is poised to be a best-in-class, potentially breakthrough therapy for treating iron deficiency without any GI side-effects (eg. nausea, constipation, diarrhea, etc.) typically associated with oral iron supplements, and without the risks, costs and side-effects associated with IV iron infusions. When approved, Keylika’s proprietary transdermal iron will be the world's first prescription iron patch. Iron deficiency is a hugely understated problem affecting about 1.6 billion people worldwide and more than 30 million in the US alone, translating to a $17B US market.", "one_liner": "Redefining the Standard of Care for Iron Deficiency Anemia", "team_size": 1, "industry": "Healthcare", "subindustry": "Healthcare -> Therapeutics", "launched_at": 1658519436, "tags": [ "AI-powered Drug Discovery", "Medical Devices", "Drug Delivery", "Nanotechnology", "Therapeutics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Healthcare", "Therapeutics" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": [ { "question": "Describe what your company does in 50 characters or less.", "answer": "We develop metallodrugs for unmet medical needs." } ], "question_answers": null, "url": "https://www.ycombinator.com/companies/keylika", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/keylika.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/keylika/", "x_url": "https://twitter.com/keylika_inc", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd12fc4dc5d053441f31a9c528579e0d16f3449e.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/58/1662662980325-y62ohje1s2-e76deefbbfbb2fb38de30a62b84546d9/714326f49aba38f91fda71a9c6d47471.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022701Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=658b0a7fb2ce8bd68597d0a5076c7260b1d48c3c6ffdde07533bb121cf26ce6b", "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 756768, "full_name": "Buddha Chaudhuri", "title": "Founder", "founder_bio": "Buddha spearheaded the technology development of a microneedle-based biosensor from lab-to-fab for a Series B stage medtech startup. Having worked across 3 continents and 6 countries, he has been instrumental in developing novel healthcare technologies in leading global research institutes and biotech companies, for the last 15 years. \n\nBuddha earned his PhD from Imec / KU Leuven in Belgium, in Drug Delivery Microsystems. He also pursued a Postdoc at UC San Francisco (UCSF) / UC Berkeley. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/buddhadevpc", "x_url": "https://twitter.com/buddhachaudhuri", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8e4a1a5185f5455f05b9967fc8793797c729bd4c.jpg" }, { "user_id": 776586, "full_name": "Frederik Ceyssens", "title": "Founder", "founder_bio": "With a deep passion for diverse technologies, I'm a true multi-domain expert. My interest go from tiny moving machines to nuclear space rockets, from electrical circuits to medical devices and beyond. Next to Keylika, I founded ReVision, a neurotech company aiming at restoring sight in blind people, developing widely useful high-res neurotech in the process.\n\nI have an EE PhD, a MSc. in IC design as well as a BSc. in Medicine. ", "is_active": true, "linkedin_url": "http://linkedin.com/in/frederik-ceyssens-43b6ab5", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/20288d3198cb324772987da4da9b7f8137d8a725.jpg" } ], "jobs": [], "news": [ { "title": "Keylika Develops World's First Buccal Patch for Iron Deficiency Anemia (IDA) | Entrepreneur", "url": "https://www.entrepreneur.com/en-au/news-and-trends/keylika-develops-worlds-first-buccal-patch-for-iron/490791", "date": "Apr 29, 2025" }, { "title": "Keylika develops first Buccal Patch for Iron Deficiency Anemia (IDA) - Arabian Business: Latest News on the Middle East, Real Estate, Finance, and More", "url": "https://www.arabianbusiness.com/industries/healthcare/keylika-develops-first-buccal-patch-for-iron-deficiency-anemia-ida", "date": "Apr 25, 2025" }, { "title": "Bootstrapped Indian Startup Innovating Treatment for Eczema, Itch & Beyond | Entrepreneur", "url": "https://www.entrepreneur.com/en-in/news-and-trends/bootstrapped-indian-startup-innovating-treatment-for/490293", "date": "Apr 20, 2025" }, { "title": "TACKLING IRON-DEFICIENCY ANAEMIA: A LEAP FORWARD WITH A REVOLUTIONARY BUCCAL PATCH - ONdrugDelivery", "url": "https://www.ondrugdelivery.com/tackling-iron-deficiency-anaemia-a-leap-forward-with-a-revolutionary-buccal-patch/", "date": "Jun 17, 2024" }, { "title": "Bootstrapping in Biotech: How Keylika’s Founding Story Highlights Key Entrepreneurship Lessons", "url": "https://www.usatoday.com/story/special/contributor-content/2024/05/12/bootstrapping-in-biotech-how-keylikas-founding-story-highlights-key-entrepreneurship-lessons/73665473007/", "date": "May 12, 2024" } ], "launches": [ { "id": 64959, "title": "Keylika: Redefining the Standard of Care for Iron Deficiency", "tagline": "World's first prescription iron patch to treat Iron Deficiency Anemia", "body": "[Keylika](https://www.keylika.com) is combining its **metallodrug synthesis technology** with **optimized delivery methods** to obtain improved clinical outcomes. Our first product is a **novel iron drug** in a **wearable skin patch** with significantly improved safety, efficacy and tolerability to treat **Iron Deficiency Anemia (IDA).** When approved, Keylika’s proprietary transdermal iron will be the **world's first** prescription iron patch.\n\n### **The Problem**\n\n**Iron Deficiency Anemia (IDA) is a hugely understated, highly prevalent problem with subpar treatments.**\n\nIron deficiency (ID) affects **30 million** people in the US alone and about **1.6 billion** worldwide. Current treatments include oral iron supplements which are typically associated with low bioavailability and poor tolerability due to mild to severe gastrointestinal (GI) side-effects (constipation, nausea, stomach discomfort, diarrhea, etc.) often leading to patient non-compliance. Also, a significant number of patients just do not absorb iron taken orally.  The second line of treatment with intravenous (IV) iron infusions is more invasive, riskier, costlier and requires hospitalization due to the risk of anaphylactic shocks.\n\n### **Our Solution**\n\nOur first product is a **new small molecule (new molecular entity)**, an **iron-containing drug** designed for **transdermal** delivery by a wearable skin patch: **NistuFe**. It is a safe, highly bioavailable iron complex with higher efficacy. A therapeutically relevant dosage is administered through the skin directly into the systemic circulation, thereby circumventing the gastrointestinal tract and all associated adverse effects. Thus, we combine the benefits of both orals and IV iron, and without their respective downsides: a drug suitable for self-administration with no GI side effects and no required hospitalization that yields a superior treatment outcome from the convenience of a patient’s home. This is slated to be a potentially **best-in-class, breakthrough therapy**. Our preliminary proof-of-concept in vivo studies in rats have demonstrated excellent results.\n\n![uploaded image](/media/?type=post&id=64959&key=user_uploads/756768/c93eab67-c28a-45c2-8a20-42843bbd0d16)\n\n### **Our Science**\n\nAt our core is a **platform synthesis technology** we have developed for synthesizing mixed ligand metal complexes. We are using this platform to generate a range of drug candidates for treating therapeutic conditions in the fields of hematology, pain and inflammation, metabolic disorders, infectious diseases and oncology. We then administer these drugs using proven delivery technologies, to effect the best clinical outcomes. Positioned at the **intersection of drug development and drug delivery**, we customize the molecular structure of the drug and its formulation to optimize the therapeutic delivery per clinical requirements.\n\n### **Target Population**\n\nOur first indication being menorrhagia, our primary target population is **women of child-bearing age** in the US who suffer from severe to moderate ID, which has an estimated prevalence of 6 million. A wide range of other patient groups adds up to a potential **30 million** users in the US alone.\n\n### **Our Ask**\n\n1\\.     If you know anyone in your social circle suffering from Iron Deficiency, we would love to talk to them!\n\n2\\.     If you are connected to biopharma companies with interests in the hematology space, could you please introduce us?\n\nPlease follow us on [LinkedIn](https://www.linkedin.com/company/keylika/)!\n\nQuestions? Feel free to reach out: [founders@keylika.com](mailto:founders@keylika.com)", "url": "https://www.ycombinator.com/launches/Gtj-keylika-redefining-the-standard-of-care-for-iron-deficiency", "votes": 59, "created_at": "2022-07-28T02:09:46.908Z" } ] }, { "id": 25321, "name": "Rosebud Biosciences", "slug": "rosebud-biosciences", "former_names": [ "Rosebud Biosciences Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4227cb01a0152cba77ed3792cf3513cba3e07e77.png", "website": "https://rosebudbiosciences.com/", "all_locations": "San Francisco, CA, USA; San Carlos, CA, USA", "long_description": "Rosebud Biosciences accelerates drug development by screening drugs against organoids (micro-organs) that have the same gene mutations as the patients. We partner with therapeutics companies to screen their drugs, and we perform our own drug discovery for rare diseases that have no existing treatments. Our organoids are also fetal-like and enable discovery of novel drug targets for pediatric diseases. This technology was validated at Stanford, published in a prestigious journal, and has already led to the discovery of a drug target in a pediatric heart disease that could not have been found using traditional disease models.", "one_liner": "Growing micro-organs to find new drugs", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1646685843, "tags": [ "AI-powered Drug Discovery", "Pediatrics", "Biotech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": [ { "question": "Describe what your company does in 50 characters or less.", "answer": "Drug discovery for pediatric genetic diseases" }, { "question": "How long have each of you been working on this? How much of that has been full-time? Please explain.", "answer": "Kitch (CEO) began working full-time on the business plan and ideas for Rosebud’s platform technology in June 2020. Evan joined full-time as cofounder and CTO in January 2021." } ], "question_answers": null, "url": "https://www.ycombinator.com/companies/rosebud-biosciences", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/rosebud-biosciences.json", "year_founded": 2020, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/rosebud-biosciences", "x_url": "https://twitter.com/rosebudbiosci", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4227cb01a0152cba77ed3792cf3513cba3e07e77.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/56/1648681514995-gvexi0m05hu-9979be20ebdf521e6bc430118e7e1c5d/ce8b239fe1aeb85682f5f1ed2ee45167.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022702Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=549d0e4a497213e193b85b522c839a972bae161410fc7dd55a6f45188deed014", "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 816458, "full_name": "Kitch Wilson", "title": "Founder", "founder_bio": "Kitch is CEO and co-founder of Rosebud Biosciences. He is a Physician-Scientist (MD, PhD) with 16 years’ experience merging human induced pluripotent stem cells with genomic and molecular technologies. A Stanford-trained molecular pathologist, Kitch was an attending physician at Stanford for 6 years before cofounding Rosebud.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kitch-wilson-1b915349/", "x_url": "https://twitter.com/KitchenerWilson", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3201669276d3e21f290cb97315d57aafddecb1ef.jpg" }, { "user_id": 836987, "full_name": "Evan Lyall", "title": "Founder", "founder_bio": "Evan is co-founder and CTO of Rosebud Biosciences. Evan is a Scientist-Engineer (PhD) with 10 years’ experience in advanced microscopy, machine learning, and data engineering. At UC Berkeley he studied the neural coding of perception and was part of a team that built the first bidirectional brain machine interface with proven single cell resolution. Before joining Rosebud he contracted for startups as a data and ML engineer.", "is_active": true, "linkedin_url": "https://linkedin.com/in/evlyall", "x_url": "https://twitter.com/evlyall", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/57adb4767421f3082963c4b755eef05c4a85d56f.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 27201, "name": "Lavo Life Sciences", "slug": "lavo-life-sciences", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/590ccfba5d4f0f97ed00fd962f2fdea307dbea2f.png", "website": "https://www.lavo.ai/", "all_locations": "", "long_description": "Lavo Life Sciences runs simulations of drug molecules on computers. Pharma companies use these simulations to guide their experiments and ultimately save time and money in the lab. This will de-risk and expedite clinical trials and FDA approval.", "one_liner": "AI for drug formulation", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1674082073, "tags": [ "AI-powered Drug Discovery", "Machine Learning", "Biotech", "Drug discovery" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Inactive", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "Unspecified" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/lavo-life-sciences", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/lavo-life-sciences.json", "year_founded": 2022, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/lavo-life-sciences/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/590ccfba5d4f0f97ed00fd962f2fdea307dbea2f.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 148651, "full_name": "Scott Swarthout", "title": "Founder", "founder_bio": "Software Engineer @ DoorDash, former co-founder of Lavo Life Sciences", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/scottswarthout", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/af903ba732a9393676eeef2136e55d10a933cf34.jpg" }, { "user_id": 1053804, "full_name": "Zach Glick", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/zach-glick-800b75219/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/54056b9978aecbffd03e4a215347e4766fa753b7.jpg" }, { "user_id": 156580, "full_name": "Derek Metcalf", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/derekmetcalf", "x_url": "https://twitter.com/derek_metcalf", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/59b13bc66f7b72fb061c26d95cd51d227f632c4a.jpg" } ], "jobs": [], "news": [], "launches": [ { "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" } ] }, { "id": 28037, "name": "Diffuse Bio", "slug": "diffuse-bio", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/961a0aa4b25ffd0049a0526af8ad2406763125f4.png", "website": "http://diffuse.bio", "all_locations": "San Francisco, CA, USA", "long_description": "Diffuse is building generative AI for protein design. Our mission is to build AI systems that engineer new and useful proteins with unprecedented control and accuracy. Our team has been behind breakthroughs in AI protein design for the past 7 years, including the first experimental validation of AI-generated proteins and diffusion models for protein structure and sequence. ", "one_liner": "Generative AI for protein design", "team_size": 10, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1673220633, "tags": [ "AI-powered Drug Discovery", "Deep Learning", "Generative AI", "Machine Learning", "Biotech" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/diffuse-bio", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/diffuse-bio.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/961a0aa4b25ffd0049a0526af8ad2406763125f4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 459396, "full_name": "Namrata Anand", "title": "Founder", "founder_bio": "I’m a bioengineer / computer scientist who’s worked on problems at the intersection of machine learning and biology. For the past several years, I’ve been working on re-envisioning the computational protein design toolkit with AI. Some highlights are the first crystal structures of AI-designed proteins and diffusion models for protein structure generation. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/namrata-a-427807188/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c568c0115ac04f5eeda9ed9ac55bb03c82d123dc.jpg" } ], "jobs": [ { "id": 57697, "title": "ML Research Engineer", "url": "https://www.ycombinator.com/companies/diffuse-bio/jobs/xqlE8GS-ml-research-engineer", "location": "Menlo Park, CA, US / Remote (US; CA)", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$90K - $130K", "equity_range": "0.10% - 0.60%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "Deep Learning", "Machine learning" ] }, { "id": 57699, "title": "Software Engineer", "url": "https://www.ycombinator.com/companies/diffuse-bio/jobs/vOf4HTO-software-engineer", "location": "Menlo Park, CA / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$90K - $130K", "equity_range": "0.05% - 0.60%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "C++", "Google Cloud", "Python", "SQL", "Google App Engine" ] } ], "news": [ { "title": "Scientists use new A.I. tech to fight diseases - YouTube", "url": "https://www.youtube.com/watch?v=bJ_Zw99TZ_w&t=168s", "date": "Feb 14, 2023" }, { "title": "A.I. Turns Its Artistry to Creating New Human Proteins - The New York Times", "url": "https://www.nytimes.com/2023/01/09/science/artificial-intelligence-proteins.html", "date": "Jan 09, 2023" } ], "launches": [ { "id": 69997, "title": "🧪 ⚛️ Diffuse Bio: Generative AI for protein design", "tagline": "At Diffuse, we build new therapeutics, vaccines, and enzymes with AI!", "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!", "url": "https://www.ycombinator.com/launches/ICz-diffuse-bio-generative-ai-for-protein-design", "votes": 65, "created_at": "2023-03-07T05:47:09.229Z" } ] }, { "id": 29051, "name": "Nanograb", "slug": "nanograb", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9ae1f1866a70286591b56aa5fd2f81bdf18af255.png", "website": "https://nanograb.com", "all_locations": "London, England, United Kingdom", "long_description": "Nanograb is a computational drug discovery company that uses AI to create novel targeted non-viral gene therapy vectors. Our platform designs unique combinations of small peptide binders attached to nanoparticles to safely deliver genetic medicines to specific cells and tissues in the body at scale. ", "one_liner": "Deliver genes to any cell with AI", "team_size": 5, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1688618716, "tags": [ "AI-powered Drug Discovery", "Nanomedicine", "Biotech", "Drug Delivery", "Therapeutics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United Kingdom", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/nanograb", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/nanograb.json", "year_founded": 2023, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/94221598", "x_url": "https://twitter.com/nanograb", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9ae1f1866a70286591b56aa5fd2f81bdf18af255.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 983241, "full_name": "Debesh Mandal", "title": "Founder", "founder_bio": "Co-founder and CEO of Nanograb. PhD in Computational Biophysics from Imperial College London. Experienced in molecular simulations and building software for scientists. First software hire at Fabricnano", "is_active": true, "linkedin_url": "https://linkedin.com/in/debeshmandal", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3ead4cccc4160ea682b2035a3c1680aa154e1808.jpg" }, { "user_id": 790668, "full_name": "Christopher Lau", "title": "Founder", "founder_bio": "Co-founder of Nanograb. M.Eng Biomaterials and Tissue Engineering at Imperial College London. Biomedical engineering experience developing biosensors using aptamers, and a drug delivery device for hypertension management.", "is_active": true, "linkedin_url": "https://linkedin.com/in/christopher-lau-542798151", "x_url": "https://twitter.com/lauhoyeung", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e715a57f758c8646639b497cdc0b29b013e14ec3.jpg" }, { "user_id": 986167, "full_name": "Shanil Panara", "title": "Founder", "founder_bio": "Cofounder and in silico lead at Nanograb; Imperial College PhD (dropout); \n\nFascinated by all things compute and physics... all in the name of figuring out real life things!\n\nOtherwise, I love singing and music in general. Fun fact: I was in an a cappella group for 5 years :)", "is_active": true, "linkedin_url": "https://linkedin.com/in/shanilpanara", "x_url": "https://www.twitter.com/shanilpanara", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/65049c750d88f435b953f0837b9dfe4cc622c836.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 115774, "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\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" }, { "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", "votes": 159, "created_at": "2023-07-29T03:10:09.478Z" } ] }, { "id": 29340, "name": "Tamarind Bio", "slug": "tamarind-bio", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/201884765e11baf051338d72f6f2e7ead2bb1fa1.png", "website": "https://www.tamarind.bio", "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.", "one_liner": "AI Inference Platform for Drug Discovery", "team_size": 20, "industry": "B2B", "subindustry": "B2B", "launched_at": 1702859345, "tags": [ "AI-powered Drug Discovery", "SaaS", "B2B", "Biotech", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/tamarind-bio", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/tamarind-bio.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/tamarind-bio/", "x_url": "https://x.com/tamarindbio", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/201884765e11baf051338d72f6f2e7ead2bb1fa1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 1224836, "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", "x_url": "https://twitter.com/kavi_deniz", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/293367f6785cbc75138799bd2af8626d1d94a319.jpg" }, { "user_id": 1451351, "full_name": "Sherry Liu", "title": "Founder", "founder_bio": "I'm currently building Tamarind Bio: easy to use computational biology tools for scientists. I studied Computer Science at Stanford. Previously, I've worked at AWS and as a machine learning researcher at Stanford. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/sherryliu987", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1b0fcfc1e14a0a5890860703a0dad8f7dacfad07.jpg" } ], "jobs": [ { "id": 69560, "title": "Software Engineer", "url": "https://www.ycombinator.com/companies/tamarind-bio/jobs/OEkMxsJ-software-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "0.30% - 0.70%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Bash/Shell", "Python", "React", "Deep Learning" ] }, { "id": 69561, "title": "Computational Biologist - Protein Engineering", "url": "https://www.ycombinator.com/companies/tamarind-bio/jobs/Sfd2dcu-computational-biologist-protein-engineering", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Bioengineering", "salary_range": "$150K - $250K", "equity_range": "0.10% - 0.50%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Python", "Deep Learning", "Protein engineering" ] }, { "id": 76280, "title": "Founding Sales Development Representative", "url": "https://www.ycombinator.com/companies/tamarind-bio/jobs/z6dyk9M-founding-sales-development-representative", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$70K - $150K", "equity_range": "0.10% - 0.50%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 79566, "title": "Infrastructure Engineer", "url": "https://www.ycombinator.com/companies/tamarind-bio/jobs/HPRZAz3-infrastructure-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$180K - $250K", "equity_range": "0.30% - 0.70%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 86594, "title": "Applications Scientist - Protein Design", "url": "https://www.ycombinator.com/companies/tamarind-bio/jobs/IyQkQvA-applications-scientist-protein-design", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Science", "role_type": "Biology", "salary_range": "$130K - $200K", "equity_range": "0.10% - 0.50%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] }, { "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", "skills": [] } ], "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" } ], "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 (), 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: \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", "votes": 16, "created_at": "2023-12-19T23:01:57.000Z" } ] }, { "id": 29341, "name": "GloGlo", "slug": "gloglo", "former_names": [ "Markprompt", "14.ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bf27e2b5fff1afcb51538ca47d0ce384847305c2.png", "website": "https://gloglo.com", "all_locations": "San Francisco, CA, USA", "long_description": "Our mission: eradicate type 1 diabetes.\r\nGloGlo is building an AI-native therapeutics company focused on accelerating the discovery of treatments that can prevent, stop, and ultimately cure type 1 diabetes.\r\nWe are developing AI research agents that continuously analyze scientific literature and biological datasets, generate and challenge hypotheses, design experiments, and run them in a wet lab to learn from experimental results.\r\nWe are building a tight \"in silico <> in vitro/vivo\" feedback loop that helps us identify better targets, run experiments more efficiently, and move faster toward therapies for type 1 diabetes.\r\n", "one_liner": "Curing and preventing type 1 diabetes", "team_size": 3, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1700867535, "tags": [ "AI-powered Drug Discovery", "Consumer Health Services", "Biotech", "E-commerce", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "Consumer" ], "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/gloglo", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/gloglo.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/gloglo-energy/", "x_url": "https://twitter.com/gloglo_energy", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bf27e2b5fff1afcb51538ca47d0ce384847305c2.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/2e7688039cb80f08c6efa6e82a43b4416445600b.png" ], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 72181, "full_name": "Marie Schneegans", "title": "Founder", "founder_bio": "co-founder GloGlo.com: curing autoimmune diseases with AI, starting with type 1 diabetes\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/marie-schneegans", "x_url": "https://twitter.com/marieschneegans", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/85a05fccd160477dc3140f8def1392421375435a.jpg" }, { "user_id": 425680, "full_name": "Michael Fester", "title": "Founder", "founder_bio": "Co-founder GloGlo.com. Previously, co-founder of Snips (AI voice platform, acquired by Sonos). Maths at Cambridge.", "is_active": true, "linkedin_url": "https://linkedin.com/in/michaelfester", "x_url": "https://twitter.com/michaelfester", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f1b9466bb4e820a48afa4b5e44d74254b46152d1.jpg" } ], "jobs": [ { "id": 112118, "title": "Founding Research Scientist Biology", "url": "https://www.ycombinator.com/companies/gloglo/jobs/8U7Ud7v-founding-research-scientist-biology", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Science", "role_type": "Biology", "salary_range": "$100K - $200K", "equity_range": "1.00% - 2.00%", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [] }, { "id": 112119, "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", "type": "Internship", "role": "Science", "role_type": "Biology", "salary_range": "$50K - $80K", "equity_range": "", "experience": null, "visa": "US citizen/visa only", "skills": [] }, { "id": 113864, "title": "Founding E-commerce Lead", "url": "https://www.ycombinator.com/companies/gloglo/jobs/iCUgvgY-founding-e-commerce-lead", "location": "San Francisco, CA, US / Remote", "type": "Full-time", "role": "Marketing", "role_type": null, "salary_range": "$50K - $200K", "equity_range": "1.00% - 2.00%", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [] } ], "news": [], "launches": [ { "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" } ] }, { "id": 29475, "name": "Evolvere BioSciences", "slug": "evolvere-biosciences", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/dd3489b7e5a5f2d3f7cc49de65917a83134da0a3.png", "website": "https://www.evolverebiosciences.com/", "all_locations": "Oxford, England, United Kingdom", "long_description": "🦠🤖 We use our computational models to make next-generation antibiotics that outcompete bacterial evolution and precisely target pathogenic bacteria, without harming good microbes or human cells. \r\n\r\n☠️ Current antibiotics stop working because bacteria evolve resistance to them. This makes drug-resistant bacteria a looming global health crisis - already killing more people than malaria and AIDS and it is getting exponentially worse 📈. \r\n\r\n🧬 Our approach leverages co-evolutionary protein-protein interaction datasets combined with AI to forecast bacterial mutations and create ‘future-proof’ antibiotics, addressing antibiotic resistance before it develops. This changes the game for how frequently society will need to make new antibiotics and how long our new antibiotics will be able to treat patients 👩‍⚕️.\r\n\r\nWe are a team of biochemists and evolutionary biologists who met at the University of Oxford. ", "one_liner": "Making Next-Generation Antibiotics that Outpace Bacterial Evolution", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Therapeutics", "launched_at": 1723833827, "tags": [ "AI-powered Drug Discovery", "Biotech", "Therapeutics", "AI", "Biotechnology" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Healthcare", "Therapeutics" ], "regions": [ "United Kingdom", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/evolvere-biosciences", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/evolvere-biosciences.json", "year_founded": 2021, "city": "Oxford", "country": "GB", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/dd3489b7e5a5f2d3f7cc49de65917a83134da0a3.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1571195, "full_name": "Adam Winnifrith", "title": "Founder/CEO", "founder_bio": "Evolvere (S24), CEO & Co-Founder, MBioChem Oxford, I work at the intersection of bioengineering, protein AI, and automation. E.S.B", "is_active": true, "linkedin_url": "https://linkedin.com/in/adam-winnifrith-b438ab182", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/47621187f511483c88214b24dcbe246a6c35c1f5.jpg" }, { "user_id": 1653867, "full_name": "Piotr Jedryszek", "title": "Founder/CTO", "founder_bio": "S24, Evolvere Biosciences, CTO and Co-founder, background in Bio and CompBio", "is_active": true, "linkedin_url": "https://linkedin.com/in/piotr-jedryszek-085991180", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/72e7c8ae1b67c791b656651e8d8c8af2fcf314a2.jpg" }, { "user_id": 1627026, "full_name": "Weronika Slesak", "title": "Founder", "founder_bio": "CSO at Evolvere (S24). Oxford biologist using evolution as an engineering tool.", "is_active": true, "linkedin_url": "https://linkedin.com/in/weronika-slesak", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/58fdd6f9b378d1a0e78b405fcdd0d2f7eb601783.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 29532, "name": "Ångström AI", "slug": "angstrom-ai", "former_names": [ "Angstrom AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d66ae246dae1ca35e8275f53019cac07d3259c99.png", "website": "https://www.angstrom-ai.com", "all_locations": "San Francisco, CA, USA", "long_description": "Angstrom AI builds GenAI-based molecular simulations to substitute wet lab experiments in the pre-clinical drug development pipeline. We are a team of 2 PhD's and 2 Professors from the University of Cambridge who decided to start a company together after we realised how to combine breakthoughs in our research in quantum-accurate models of physics and generative AI models.\r\n\r\nOur Biotech/Pharma clients can verify the efficacy and safety of new drug candidates using our computer simulations, which match the accuracy of wet lab experiments, but are over 100x faster. We achieve this accuracy by constraining our genAI-based simulations to obey the laws of physics, avoiding the hallucinations seen in other GenAI technologies.\r\n\r\nSince joining YC, Angstrom AI has developed the first physically accurate gen-AI based simulation of multiple molecules interacting. We have published the first molecule water solubility results with accuracy within the error range of wet lab experiments. We have also kicked-off a 150K pilot project with a pharma company to apply our tech to estimating solubility in their drug development pipeline.", "one_liner": "Gen AI molecular simulations that reproduce wetlab results", "team_size": 5, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1719350589, "tags": [ "AI-powered Drug Discovery", "Biotech", "Drug discovery", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/angstrom-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/angstrom-ai.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/angstrom-ai/", "x_url": "https://x.com/Angstrom_ai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Angstrom-AI", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/d66ae246dae1ca35e8275f53019cac07d3259c99.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/233f98e0b4c858fd18363ab1100b645bbdbec75a.png" ], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 1757871, "full_name": "Jose Miguel Hernandez Lobato", "title": "Founder", "founder_bio": "Miguel is co-founder of Ångström AI and Professor of Machine Learning at the Department of Engineering, University of Cambridge, UK. He has nearly 20 years of research experience in Machine Learning and his work has been cited over 15,000 times. His research in Machine Learning for Molecules has led him to build strong relationships with partners in BioTech and Pharma. Before joining Cambridge as a faculty, he was a postdoctoral fellow at Harvard University.", "is_active": true, "linkedin_url": "https://linkedin.com/in/jose-miguel-hernandez-lobato-7790512a5", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/84e9366cdd7032b2d7a84d9b9c9c393c38bb2c04.jpg" }, { "user_id": 454695, "full_name": "Laurence Midgley", "title": "Founder/CTO", "founder_bio": "I'm the co-founder of Ångström AI which substitutes wet lab experiments with physically accurate GenAI molecular simulations for clients in Pharma and BioTech. Before founding Ångström AI, I was a research engineer at InstaDeep (acquired by BioNTech 2023), and pursued a PhD at the University of Cambridge in generative AI models for molecular systems. I love coding and surfing, reach out if you are in the Bay Area and want to catch some waves.", "is_active": true, "linkedin_url": "https://linkedin.com/in/laurencemidgley", "x_url": "https://twitter.com/SilkyDogfish", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/acb4d76179a92480410f2827c5c18dc175371e3f.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 81767, "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. \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\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   \n\n\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", "votes": 35, "created_at": "2024-06-26T16:24:53.448Z" } ] }, { "id": 30689, "name": "b12 Labs", "slug": "b12-labs", "former_names": [ "B12", "b-12" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/64c9b960693fdd85d6fb6be6fefc443ca4ab80af.png", "website": "https://b12-labs.com/", "all_locations": "San Francisco, CA, USA", "long_description": "b12 makes high-value molecules that are otherwise hard to make. We help pharma companies plan and make new drugs, accelerating early-stage drug discovery from years to months.\r\n\r\nAndres built the first AI agent that can autonomously think and make molecules in a robotic lab, published in Nature Machine Intelligence (1000+ citations), and won the best paper award at the NeurIPS AI for Science conference.\r\nZlatko is a 3x National Chemistry Olympiad champion, competed at IChO level, and medicinal chemist who has worked at Roche and leading lab robotics companies. \r\n\r\nCombined, we have an extensive network of hundreds of chemists, automation scientists, and AI 4 Science researchers.\r\n\r\nWe run paid pilots with big pharma companies, demonstrating that our platform planned a set of experiments that achieved full conversion in a single attempt, eliminating the iteration cycle.", "one_liner": "Making hard molecules for Pharma and Biotech", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1753004647, "tags": [ "AI-powered Drug Discovery", "Robotics" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "Andres and Zlatko met at EPFL, while working in the Lab of Artificial Chemical Intelligence. Zlatko was doing his postdoc and Andres was doing his PhD." }, { "question": "How did you decide to apply to Y Combinator? What was your experience applying, going through the batch, and fundraising at demo day?", "answer": "We decided to apply after we had a YC partners visit our campus at EPFL. Experience was very intense, a lot of work and great advice on how to build a startup. " } ], "url": "https://www.ycombinator.com/companies/b12-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/b12-labs.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/108178057/admin/dashboard/", "x_url": "https://x.com/b12Labs", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/64c9b960693fdd85d6fb6be6fefc443ca4ab80af.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/2086595d1ba11517239f8da752e3e320cd135a61.png" ], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 2507050, "full_name": "Zlatko Jončev", "title": "Founder/CEO", "founder_bio": "at b-12 we are developing AI agents for automated workflows in chemistry and pharma R&D, as a way of manifestating chemical super intelligence in physical world.\n\nour long-term goal is to create autonomous labs that can perform complex, multistep syntheses with minimal human intervention.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/zlatkojoncev/", "x_url": "https://x.com/ZJoncev", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3dbd05f37d1de0578d9ceb0b4ef177c4ad19375c.jpg" }, { "user_id": 2507280, "full_name": "Andres Bran", "title": "Founder/CTO", "founder_bio": "Before b-12, Andres built the first AI agent to plan and make molecules in a robotic lab (750+ cites in Nature Machine Intelligence, and Best Paper at NeurIPS AI for Science), along with several other works in agents and LLMs for chemistry (1100+ citations).\nHe was an Intern at Max Planck (Math in Science), and PhD at EPFL (AI+Chemistry).\n\nAutomation is the future of chemistry, but integration and human operability is the hidden demon nobody talks about. We're fixing that at b-12.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/andrescmb", "x_url": "https://x.com/drecmb", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/88d3dfc21250265c0b0b6f6f758fa7494e23fbfa.jpg" } ], "jobs": [ { "id": 105109, "title": "Founding Product Engineer (Full Stack)", "url": "https://www.ycombinator.com/companies/b12-labs/jobs/IEX0tV9-founding-product-engineer-full-stack", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$120K - $160K", "equity_range": "0.50% - 2.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 120586, "title": "b12 Foundry - We Make the Molecules AI Designs", "tagline": "AI Chemistry Foundry - from a molecule on a screen to a compound in a vial", "body": "![uploaded image](/media/?type=post&id=120586&key=user_uploads/2507050/a1d7d999-eef2-4350-86a4-f299ef904756)\n\n\\\n**TL;DR:**\n\nAI can design a molecule in seconds, but making it can still take a chemist weeks. b12 Foundry turns a molecule on a screen into a compound in a vial, in a fraction of the time it would take a chemist. A combination of state-of-the-art AI agents plans the route, designs the experiments and runs them on robots in our partner lab, so you get your molecules, and clean data including failed reactions, without needing a lab of your own. **Last year we launched here as a chemistry copilot. Today we work with several large companies, across pharma, flavor and fragrance, energy and biotech industries and universities, and the Foundry is actively making molecules for clients.**\n\n#### ❌ **The problem:**\n\n**AI designs molecules faster than anyone can make them:**\n\n* AI models can now design a molecule in seconds, but making it can still take a chemist weeks.\n* The most interesting designs sit outside the catalogues vendors build from simple, reliable reactions, so someone has to make each one by hand.\n* In practice, teams end up testing an easier cousin of the molecule their model wanted, or nothing at all.\n\n**Good chemistry data is hard to find:**\n\n* Published data mostly shows the reactions that worked, so the failures that models need to learn from rarely get recorded.\n* Running clean, consistent experiments at scale takes a lab, robots and people that most AI teams don't have.\n\nIf you're building AI for drug discovery or materials, synthesis is quickly becoming your bottleneck.\n\n## ✅ **Our Solution:**\n\n\n\n**b12 Foundry** combines state-of-the-art AI agents with robots in our partner lab, so we can **plan** and **run** complex chemistry for you, end to end.\n\n**How it works:**\n\n* **Send us a molecule or a workflow:** Share a structure your model designed, or an experiment you want run.\n* **Our agents plan the chemistry:** They work out how to make the molecule and design the experiments for the hardest steps.\n* **Robots run it in our partner lab:** The agents run every experiment on the robots, including chemistry that is hard to do by hand.\n* **You get compounds and data back:** You receive the molecules plus every result, failed reactions included, and everything we run for you belongs to you.\n\n## **👥 Our Team:**\n\nWe are Zlatko and Andres, and in the past, we have:\n\n1. Worked with big pharma (Roche), Chemical Robotics ([**Chemspeed**](https://chemspeed.com/)), and big chemical data ([**MaxPlanck**](https://www.mis.mpg.de/));\n2. Pioneered [**AI agents**](https://www.nature.com/articles/s42256-024-00832-8) for chemistry and [**retrosynthesis**](https://arxiv.org/abs/2503.08537) (at EPFL);\n3. Invented [**new chemistry**](https://onlinelibrary.wiley.com/doi/10.1002/anie.202211168).\n\nWhen we launched here last year, we promised a full stack from planning to robotic execution. Since then, our customer base has grown well beyond pharma. Alongside three large pharma companies, including a top-3 global pharma that came back as a repeat customer, we now work with a flavor and fragrance leader, energy companies, biotech companies, and universities around the world. An AI drug discovery platform has also built our synthesis agent into its own pipeline, and this summer we opened the Foundry, which is actively making molecules for clients.\n\nThe team has grown with it. **Adriana Faraone**, our Research Scientist, runs the experiments in the Foundry, and **Victor Sabanza Gil** joins us from his PhD at EPFL, where he worked on [**Bayesian optimization**](https://arxiv.org/abs/2410.00544) for molecules and materials, the kind of AI that decides which experiment to run next. Together, we give anyone with a molecule on a screen extra **brainpower** and an **additional pair of hands**.\n\n## 🙏 Our Ask:\n\nDo you know a biotech or AI-for-science team whose molecules are hard to make, or who needs experimental chemistry data? We would love a warm intro!\n\nIf you've set up a wet lab in the Bay Area, we'd love 20 minutes of your advice, because our own lab here is next.\n\nSend us an email at: [founders@b12-labs.com](mailto:founders@b12-labs.com) or send them to our website: [b12-labs.com](http://b12-labs.com)\n\n#### ", "url": "https://www.ycombinator.com/launches/VMw-b12-foundry-we-make-the-molecules-ai-designs", "votes": 6, "created_at": "2026-10-08T13:52:26.985Z" }, { "id": 92473, "title": "b-12 - Chemistry Copilot for Pharma & Biotech", "tagline": "Full Stack Chemistry Platform - from planning to robotic execution of experiments", "body": "**TL;DR:**\n\nDrug discovery is slow, and it costs billions of dollars. The bottleneck in early discovery is chemical synthesis. b-12 is building a full stack of AI agents to deliver planning and robotic executions of experiments, in minutes, not months. **Our team has developed custom robots for chemistry (Chemspeed), orchestrated AI agents for lab control (EPFL & IBM), and is translating this into a powerful and easy-to-use product.**\n\n**Ask:** Are you running chemistry experiments or chemical robots? Do you know someone who is? Email us at [founders@b12-labs.com](mailto:founders@b12-labs.com) to be added to our pilot program. See below for details.\n\nhttps://www.youtube.com/watch?v=qmf7q-Sie2o\n\n# ❌ **The problem:**\n\n**Drug Discovery is slow:**\n\n* It takes 10+ years and over $2 billion to discover a new drug.\n* 2 in 10000 compounds make it.\n* Chemistry execution is very slow.\n\nExperiments take time! Designing good experiments and trial and error in the lab are big factors.\n\n![uploaded image](/media/?type=post&id=92473&key=user_uploads/2507050/0cd3f202-e48b-48da-a6e5-3ae1572cddab)\n\n**Software for chemists is fragmented:**\n\n* There are many solutions for many different parts of the pipeline.\n* Tools are outdated, and integration is largely missing.\n\n**Lab robots are underused:**\n\n* Most lab robots are _not accessible to scientists_ because they are hard to use.\n* Drag and drop software for multimillion-dollar robotic labs.\n* Lab automation engineers are hard to find.\n\n**Current State:**\n\n![uploaded image](/media/?type=post&id=92473&key=user_uploads/2507050/2a3d91a0-8e78-44d9-a89c-f1dcc7b45f67)\n\n**b-12:**\n\n![uploaded image](/media/?type=post&id=92473&key=user_uploads/2507050/893d0cd2-d071-43be-b08e-ddf29b8aaddc)\n\n# ✅ **Our** **Solution:**\n\nWe are integrating a full stack of Agents to **design experiments** and **write code for the robot** that runs these experiments, with the tools and context needed to predict chemically meaningful results, and close the experimental loop.\\\n\\\n**Key capabilities of our Agents:**\n\n* **Streamline Experiment Planning**: Our tools accelerate creation of experimental details based on literature and expert tools, and tailor the output to the user’s specific needs.\n* **Unified platform:** Our platform integrates several agents that are in tight communication and can go from natural language input to robotic execution in minutes.\n* **No coding required - use natural language:** With arbitrary input flexibility, we can control the robot the same way you would talk to your lab-mates, including inputs from previous runs, and uploading relevant documents.\n* **Parallel and scalable to any lab:** We are vendor agnostic, and integrate with different robotics (Chemspeed, Unchained, Opentrons, etc.) and labs performing experiments manually.\n\n**Pilot Program:** Our team will work on integrating Agentic solutions tailored to your lab's specific problems, whether you use lab automation or not. We want to speed up molecular discovery.\n\n# **👥 Our Team:**\n\n![uploaded image](/media/?type=post&id=92473&key=user_uploads/2507050/92133d6c-ba4b-4722-9115-8c45405366aa)\n\nWe are Zlatko and Andres, and in the past, we have:\n\n1. Worked with big pharma (Roche), Chemical Robotics ([**Chemspeed**](https://chemspeed.com/)), and big chemical data ([**MaxPlanck**](https://www.mis.mpg.de/));\n2. Pioneered [**AI agents**](https://www.nature.com/articles/s42256-024-00832-8) for chemistry and [**retrosynthesis**](https://arxiv.org/abs/2503.08537) (at EPFL);\n3. Invented [**new chemistry**](https://onlinelibrary.wiley.com/doi/10.1002/anie.202211168);\n\nNow we want to integrate it into a seamless platform, where anyone who wants to run experiments gets extra **brainpower** and an **additional pair of hands**.\n\n# 🙏 Our Ask:\n\nDo you know anyone in chemistry working in the pharmaceutical or biotech industry?\n\nWe would love a warm intro!\n\nSend us an email at:\n\n[founders@b12-labs.com](mailto:founders@b12-labs.com) or send them to our website: [b12-labs.com](http://b12-labs.com)", "url": "https://www.ycombinator.com/launches/O3V-b-12-chemistry-copilot-for-pharma-biotech", "votes": 37, "created_at": "2025-07-31T16:18:49.754Z" } ] }, { "id": 30747, "name": "Convexia", "slug": "convexia", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c68bf871c0fe363c6048343df283d9282ab990f4.png", "website": "https://convexia.bio", "all_locations": "San Francisco, CA, USA", "long_description": "An AI-maximalist pharma platform. We use agents to buy drugs, run clinical trials, and sell for a profit. 10x faster and 20x leaner than incumbents.\r\n\r\nSourcing Agent: Mines public/private databases and unstructured global data to surface overlooked preclinical candidates.\r\nScientific Agent: Runs comp bio models (ESM-3, RFdiffusion, Boltz-2, AlphaFold) to assess safety and efficacy in silico.\r\nCommercial Agent: Analyzes FDA incentives, pricing dynamics, TAM, competitive landscape, and payer alignment.\r\nClinical Agent: Runs digital twin simulations, evaluates CRO/CMC risk, and builds trial plans to Phase 1.\r\nPoS Agent: Evaluates the most critical factors that impact the likelihood of clinical trial success. \r\n\r\nBuilt by 2 Stanford CS students who have built 3 startups together before. ", "one_liner": "The world's first AI-maximalist pharma company", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1752301183, "tags": [ "AI-powered Drug Discovery", "Biotech", "Drug Delivery" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "Ayaan and Rahul met at an entrepreneurship conference in 8th grade and have been building together ever since. By the end of high school, they’d launched two startups. At Stanford, they built a third, recently acquired. Now they’re on their fourth venture - and this is the one they believe can be a unicorn." } ], "url": "https://www.ycombinator.com/companies/convexia", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/convexia.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/convexia-bio", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c68bf871c0fe363c6048343df283d9282ab990f4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 2433930, "full_name": "Ayaan Parikh", "title": "Founder/CEO", "founder_bio": "Building agents to find and acquire early-stage drug assets. \n \nStanford CS + Bio\n\nBuilt 3 healthtech startups (telemedicine, addiction recovery, hydroponic systems). \n\nPublished 4 papers on molecular dynamics. \n\nBiotech VC and consulting experience. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ayaan-parikh", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ae7004ae9a37271f3847d9919c6c0cd92a310ac2.jpg" }, { "user_id": 2712957, "full_name": "Rahul Vijayan", "title": "Founder/CTO", "founder_bio": "Building agents to find and acquire early-stage drug assets.\nStanford CS + Econ \nBuilt 3 healthtech startups (telemedicine, gamified recovery, hydroponic systems).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rahulvijayan28/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b62fd8479d819d4020ab4de6ddaa6a655eccfd2a.jpg" } ], "jobs": [], "news": [], "launches": [ { "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" } ] }, { "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": 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.", "one_liner": "AI and Data for Cancer Therapeutics", "team_size": 8, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1770167044, "tags": [ "AI-powered Drug Discovery", "Biotech", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/origin-bio", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/origin-bio.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/origin-bio-inc/", "x_url": "https://x.com/origin_bio", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3f0bdde9261c1483c617c9ca235a7378e3d7ba10.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 552024, "full_name": "Yash Rathod", "title": "Founder/CEO", "founder_bio": "CEO @ Origin | Computer Science @ UIUC, Computer Vision and Reinforcement Learning Research, First Prize 2022 OpenCV AI Research Competition.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/yrraadi", "x_url": "https://x.com/yrraadi", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8b46413aa00b725fde17f09bbd006abf2e62de7d.jpg" }, { "user_id": 1327757, "full_name": "Malhar Bhide", "title": "Founder/CTO", "founder_bio": "CTO @ Origin | Prev: Computer Science @ UIUC, ML Research @ Wadhwani AI, Automorphic (YC S23). Published disease modeling research done in high school in Nature Scientific Reports.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/malhar-bhide-9002791a7/", "x_url": "https://x.com/malhar317", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0819722e70db62421af0ad70c9116c693f24efcd.jpg" } ], "jobs": [ { "id": 112652, "title": "Computational Biologist, Spatial Transcriptomics", "url": "https://www.ycombinator.com/companies/origin-bio/jobs/a7q1q8R-computational-biologist-spatial-transcriptomics", "location": "San Francisco, CA, US / Remote", "type": "Full-time", "role": "Science", "role_type": "Biology", "salary_range": "$140K - $170K", "equity_range": "0.25% - 0.50%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "R", "Data Analytics", "Bioinformatics", "Genomics", "Biology" ] }, { "id": 116342, "title": "Head of Data Partnerships", "url": "https://www.ycombinator.com/companies/origin-bio/jobs/9BFC3r8-head-of-data-partnerships", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$200K - $250K", "equity_range": "1.00% - 1.50%", "experience": "6+ years", "visa": "Will sponsor", "skills": [ "Machine Learning", "Data Analytics", "Bioinformatics", "Biotechnology", "Genomics", "Drug development" ] } ], "news": [], "launches": [ { "id": 97480, "title": "Origin - Safer Cell & Gene Therapies with AI", "tagline": "AI designed regulatory DNA sequences to program gene expression patterns", "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\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\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)", "url": "https://www.ycombinator.com/launches/PMG-origin-safer-cell-gene-therapies-with-ai", "votes": 18, "created_at": "2026-02-04T21:33:23.276Z" } ] }, { "id": 31425, "name": "WonderTx", "slug": "wondertx", "former_names": [ "A Company in Stealth Mode Incorporated" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/08d90d66366f5066cf3ad5ae02f156dc4bcc8ebe.png", "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" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Healthcare", "Therapeutics" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "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. " } ], "url": "https://www.ycombinator.com/companies/wondertx", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/wondertx.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/wondertx/", "x_url": "https://x.com/WonderTxAi", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/08d90d66366f5066cf3ad5ae02f156dc4bcc8ebe.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "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/", "x_url": "https://twitter.com/AbeHeifets", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/571a64309f08a9ce37f2b403541f4d87c8410311.jpg" } ], "jobs": [], "news": [], "launches": [ { "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 & 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" } ] }, { "id": 31532, "name": "Atlas Discovery", "slug": "atlas-discovery", "former_names": [ "Rhizome Bio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6e6c174c3a8e56844bf81efbc8b8532b4e509f73.png", "website": "https://atlasdiscovery.bio", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "AI-native pharma company", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1782247231, "tags": [ "AI-powered Drug Discovery", "Drug discovery", "Biotechnology" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/atlas-discovery", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/atlas-discovery.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/atlas-discovery", "x_url": "https://x.com/atlasdiscovery0", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6e6c174c3a8e56844bf81efbc8b8532b4e509f73.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 774885, "full_name": "Shaamil Karim", "title": "Founder/CEO", "founder_bio": "Excited about AI for predictive biology", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shaamil-karim/", "x_url": "https://x.com/shaamilkarim1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/46de7a475d51ebe79a88352728e36af1849266d8.jpg" }, { "user_id": 2088957, "full_name": "Christian Gensbigler", "title": "Founder", "founder_bio": "Previously MTS at Origin Bio (YC w26), theoretical biology at Johns Hopkins School of Medicine, math at Dartmouth ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/christian-g-a2594029", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/09fc09f66416e77a682ab3ae047ba7f436df4d44.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 107031, "title": "Atlas Discovery: AI-native pharma company", "tagline": "Autonomous research agents and models for drug development", "body": "TL;DR: We’ve built the foundations of a pharma in the age of superintelligence: data partnerships, biology models and autonomous research agents. We have SOTA results on finding overlooked drugs and predicting clinical trial success rates. \n\nHey everyone! We ([Sanjukta Bhattacharya](https://www.linkedin.com/in/sanjukta-b-82b541198/), [Christian Gensbigler](https://www.linkedin.com/in/christian-g-a2594029a/), and I) started Atlas Discovery to get treatments to patients who need them most.\n\n**The problem:** Today's pharma works for only \\~150 diseases. Below roughly $150M in expected revenue a program never gets funded, because the cost of developing a drug is fixed by the knowledge work it requires, not the size of the market. 10,000+ diseases remain without an approved treatment even where the biology is tractable. Meanwhile, there are 100,000+ existing drugs, and barely 1% of the drug-disease combinations have ever been tried. The bottleneck is no longer supply of compounds; it's the judgment needed to search that space and run the trials, and the removal of human coordination costs.\n\n**Our** **thesis** is that AI will drop the cost of drug development enough to push the viable threshold from \\~$150M toward \\~$15M, opening a \\~$500B market incumbents have structurally ignored. A company built for that world needs three things: proprietary data, models that predict clinical trial outcomes, and agent systems that replace knowledge work. We use agents to repurpose existing drugs for the long tail of diseases, then run the clinical trials faster and cheaper. \n\n**Progress**: In three months we've built the first version of all three layers. Data: an 8 figure LOI with an AI lab and 6 figures in contracted revenue. Models: state-of-the-art clinical trial prediction results published at three conferences,. Agents: three rare disease foundations are now testing drugs surfaced by our agents. \n\nOn drug repurposing using agents, we see nearly 5x improvement over current frontier LLMs. Read more about this benchmark [here](https://atlasdiscovery.bio/repurposingbench.html).\n\n![uploaded image](/media/?type=post&id=107031&key=user_uploads/774885/cf98d216-1d2c-45a0-99c9-ef87d2b7b296)\n\n\nWe've also shown that models trained on in vitro cell data (Latin for \"in glass\") and mouse data can translate to gains in predicting patient response to drugs in human clinical trials.\n\n![uploaded image](/media/?type=post&id=107031&key=user_uploads/774885/6813bfeb-e5c9-47ec-9b9e-89362cf8eb15)\n\nWe’ve also shown SOTA results in being able to predict clinical trial success when trained on over 100,000 clinical trials data and evaluated on a held out set of 10,000 trials. Read more [here](https://atlasdiscovery.bio/clinical-trial-prediction-with-ai-agents.html)\n\n![uploaded image](/media/?type=post&id=107031&key=user_uploads/774885/23aff312-7161-4be9-b4af-3273d469bd5d)\n\nWe’ve presented our research in 3 top conferences (ICLR, CSHL, ICML) and shown that new AI architectures like discrete diffusion can learn these gene expression patterns and predict response to treatments with over 10x improvement in accuracy while training on 50x less data.\n\n![uploaded image](/media/?type=post&id=107031&key=user_uploads/774885/fa2a6593-751d-448b-a67b-e232e3e07ffb)\n\nReach us at [founders@atlasdiscovery.bio](mailto:founders@atlasdiscovery.bio)\n\nBlog: blog", "url": "https://www.ycombinator.com/launches/RqJ-atlas-discovery-ai-native-pharma-company", "votes": 7, "created_at": "2026-07-24T17:07:56.608Z" } ] }, { "id": 35883, "name": "Halmos Labs", "slug": "halmos-labs", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/093fdc6d2122e7140cab391f450941fd3922b145.png", "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": [ "AI-powered Drug Discovery", "Swarm AI", "Biotech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Healthcare", "Industrial Bio" ], "regions": [ "Sweden", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/halmos-labs", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/halmos-labs.json", "year_founded": 2026, "city": "Lund", "country": "SE", "linkedin_url": "https://www.linkedin.com/company/144861296", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/093fdc6d2122e7140cab391f450941fd3922b145.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Kulveer Taggar", "url": null }, "founders": [ { "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.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mattias-akke-40a83219a", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e01b9b8a380ee646dee214cd9e121e4c7102388a.jpg" }, { "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/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/17a47ee26b59bf606fe75eb4b38cf5062197f685.jpg" } ], "jobs": [], "news": [], "launches": [ { "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\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&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&D tasks**\\\nWe start by designing experiments. From there we take on increasingly large R&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&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" } ] } ]