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The company unites computational chemistry, structural biology, and medicinal chemistry to pioneer the next generation of AI-driven drug discovery platforms.", "one_liner": "Artificial intelligence for drug discovery.", "team_size": 67, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1416219336, "tags": [ "AI-powered Drug Discovery", "Deep Learning", "Biotech", "Drug discovery", "Oncology" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2015", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/numerion-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2015/numerion-labs.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/atomwise/", "x_url": "https://twitter.com/atomwiseinc", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/atomwise", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c3b218096497a702becc32895ca1b21c4cae78bd.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "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": false, "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" }, { "user_id": 10392, "full_name": "Alex Levy", "title": "Founder", "founder_bio": "Leveraging AI to revolutionize the physical sciences, Alex Levy is the CEO and co-founder of Atmo, the leading company integrating AI into global weather forecasting. Previously, he co-founded Atomwise (W15), a pioneering AI company in small molecule drug discovery, boasting over 800 active programs and $1 billion in pharmaceutical partnerships. An alumnus of the University of Toronto, he began his career by leading a computer vision research lab within the Computer Science Department.", "is_active": false, "linkedin_url": "ca.linkedin.com/in/levyalex", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9d6b2d29f328539e24d2cb0bac16217cc3f8838b.jpg" }, { "user_id": 10399, "full_name": "Izhar Wallach", "title": "Founder", "founder_bio": "-", "is_active": false, "linkedin_url": "https://www.linkedin.com/pub/izhar-wallach/11/38/a04", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0dd953a0f7260cfc91ec2e0021a7c37e4238d45d.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 857, "name": "Shasqi", "slug": "shasqi", "former_names": [ "Shasqi", "Tambo" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ffc67fc2138abb0d2b24dd42fa7b056c024ad80a.png", "website": "", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Shasqi is leading the development of therapeutics leveraging click chemistry to get active cancer treatments to tumors with its proprietary CAPAC™ platform. Our technology is based on localizing a click chemistry reagent in the tumor area, which then activates a second agent, a systemically infused protodrug. When the two 'click', a powerful localized therapy is activated at the tumor, while keeping its systemic exposure levels below toxic thresholds.", "one_liner": "Targeting Cancer with Click Chemistry", "team_size": 28, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1421189619, "tags": [ "Biotech", "Therapeutics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2015", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/shasqi", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2015/shasqi.json", "year_founded": 2015, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/shasqi-inc./", "x_url": "https://twitter.com/shasqi", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/shasqi", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ffc67fc2138abb0d2b24dd42fa7b056c024ad80a.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 71821, "full_name": "Jose M. Mejia Oneto", "title": "Founder/CEO", "founder_bio": "José M Mejía Oneto is a physician scientist with training in Organic Chemistry at Emory and Orthopedics at UC Davis. During his clinical training, Jose recognized the need for a better way to get drugs to the right location in the body. Joined the YC program in 2015 with Tambo and in 2018 its spinoff Shasqi, focussing on pioneering click chemistry in humans for better cancer drugs. Shasqi has raised a more than $60M in funding, with its lead asset in Phase 2 and a growing pipeline of drugs. 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Verge has an end-to-end technology-driven drug discovery and development platform, featuring one of the field’s largest and most comprehensive proprietary patient genomics datasets from human tissue. Verge uses machine learning to mine this data and develops these insights into new drugs using it’s human-centric biology and chemistry platforms. ", "one_liner": "We use human data and AI to develop better drugs faster.", "team_size": 50, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1430156118, "tags": [], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2015", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/verge-genomics", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2015/verge-genomics.json", "year_founded": 2015, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/verge-genomics/", "x_url": "https://twitter.com/vergegenomics", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/verge-genomics", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c4cfdad3584cced8a30fec859292e2380372a30d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/1d5bc4370e34d1fa143dcf4389a98e604308d530.png" ], "primary_partner": null, "founders": [ { "user_id": 75859, "full_name": "Alice Zhang", "title": "Founder/CEO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/pub/alice-zhang/2b/786/208", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fce1d30cb341b8c42af81a3d7f577be552dc6ff1.jpg" } ], "jobs": [], "news": [ { "title": "Biotech begins human trials with drug discovered using AI", "url": "https://www.ft.com/content/0006ae3f-7064-4aa6-98cd-8912f544acc5", "date": "Oct 31, 2022" }, { "title": "Alice Zhang’s ‘all-in-human’ AI and genomics upstart scores a $98M Series B – Endpoints News", "url": "https://endpts.com/alice-zhangs-all-in-human-ai-and-genomics-upstart-scores-a-98m-series-b/", "date": "Dec 16, 2021" }, { "title": "Verge Genomics Inks Multimillion-Dollar Deal With Lilly To Discover New ALS Drugs", "url": "https://www.forbes.com/sites/leahrosenbaum/2021/07/08/verge-genomics-inks-multi-million-dollar-deal-with-lilly-to-discover-new-als-drugs/?sh=4afea1c4f18f", "date": "Jul 12, 2021" } ], "launches": [] }, { "id": 916, "name": "zPREDICTA", "slug": "zpredicta", "former_names": [ "Ixchel Scientific" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/02c9913df4688a7af8c4ac4519928e3912a7a8d4.png", "website": "http://zpredicta.com", "all_locations": "San Francisco, CA, USA; San Jose, CA, USA", "long_description": "zPREDICTA is developing organ-specific, physiologically-relevant 3D cell culture models providing a 1-to-1 reconstruction of human tissues. This is the only available 3D culture approach that comprehensively accounts for the major components of the cellular and extracellular tissue microenvironment preserving the critical interactions between a tumor and its surroundings. Clinical study data demonstrate the high correlation between drug efficacy measured in our platform and the clinical response. The ability of the zPREDICTA platform to reliably predict clinical outcomes offers superior compound attrition management, empowering researchers to exclude ineffective compounds while selecting agents with a high probability of success in the clinic.\r\n\r\nOur approach is compatible with any drug class, multiple tissue/cell types and a wide variety of readouts. Our customers are using these models for efficacy screening of anticancer compounds, including immuno-oncology agents, evaluation of mechanisms of drug resistance, the rescue of failed drug candidates, assessment of off-target toxicity, and a multitude other applications.", "one_liner": "Patient-derived 3D tissue reconstruction models for preclinical…", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1430156124, "tags": [], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2015", "status": "Acquired", "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": [ { "question": "How long have each of you been working on this? How much of that has been full-time? Please explain.\n", "answer": "The PD-3D™ model was developed, tested, and validated (both in cell culture and in 2 animal models) over the last 4 years. We did not write any code, but thousands of pipet tips have been used-up to develop our technology." }, { "question": "What is your company going to make? Please describe your product and what it does or will do.\n", "answer": "Our customer described our product as: “a clinical trial in a test tube.” Greater than 90% of anticancer drugs do not reach the market because they fail during clinical development. Conventional preclinical systems (cell culture or animal models) do not test drug response under physiological conditions of human tissues and do not eliminate compounds with a high likelihood of clinical failure. Our proprietary Patient-Derived 3D Tissue Culture (PD-3D™) platform is a missing link between the conventional drug testing methods and human trials. PD-3D™ is a ready-to-use organ-specific platform that mimics human tissue environment. In validation testing PD-3D™ was a reliable indicator of drug response and potential drug toxicity. Using PD-3D™, our customers can evaluate drug candidates and drugs combinations within the native environment of human tissues to predict the likelihood of clinical success. The PD-3D™ platform will serve the biotechnology and pharmaceutical market where it will be instrumental for prioritizing drug development schedules. Incorporating PD-3D™ into the preclinical testing paradigm will decrease the timelines and reduce the cost of drug development by 15-35%." } ], "question_answers": null, "url": "https://www.ycombinator.com/companies/zpredicta", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2015/zpredicta.json", "year_founded": 2014, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/zpredicta", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/ixchel-scientific", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/02c9913df4688a7af8c4ac4519928e3912a7a8d4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 84296, "full_name": "Mukti Parikh", "title": "Founder/CTO", "founder_bio": "", "is_active": false, "linkedin_url": "https://www.linkedin.com/profile/view?id=18153183&trk=nav_responsive_tab_profile", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e12da3afe3dadcacd0109ad5131d113619aec0f2.jpg" } ], "jobs": [], "news": [ { "title": "Predictive Oncology Acquires zPREDICTA, Inc.", "url": "https://www.globenewswire.com/news-release/2021/12/01/2344122/37244/en/Predictive-Oncology-Acquires-zPREDICTA-Inc.html", "date": "Dec 01, 2021" } ], "launches": [] }, { "id": 1100, "name": "Perlara", "slug": "perlara", "former_names": [ "Perlstein Lab PBC" ], "small_logo_thumb_url": "/company/thumb/missing.png", "website": "http://perlara.com", "all_locations": "San Francisco, CA, USA; Oakland, CA, USA; Remote", "long_description": "Perlara 1.0 (Feb 2014 - Feb 2019) launched early R&D joint ventures called PerlQuests with highly motivated rare disease families, companies or impact investors. 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Indee Labs is developing a non-viral intracellular delivery system based on microfluidic vortex shedding (µVS) to rapidly deliver nucleic acids, proteins and gene editing complexes to patient and donor T cells with minimal perturbation.", "one_liner": "Enabling T cell immunotherapy discovery, development and manufacturing", "team_size": 5, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1481766629, "tags": [ "Gene Therapy", "Microfluidics", "Cell Therapy" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2017", "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/indee-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2017/indee-labs.json", "year_founded": 2015, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/indee-/", "x_url": "https://twitter.com/indeelabs", "fb_url": "https://www.facebook.com/indeelabs/", "cb_url": "https://www.crunchbase.com/organization/indee-2", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ac1b20e8e540ce71927b19ed6cbc32f9956f9290.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 168592, "full_name": "Ryan Pawell", "title": "Founder/CEO", "founder_bio": "My career aim is to use technology to improve health outcomes. 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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. 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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": 26311, "name": "WhiteLab Genomics", "slug": "whitelab-genomics", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ba69864f65b85fda9f05c3280bc3b39f42500013.png", "website": "https://whitelabgx.com", "all_locations": "Paris, Île-de-France, France", "long_description": "WhiteLab Genomics is a TechBio company using artificial intelligence to design the building blocks of genomic medicines. Its proprietary ALFRED platform designs viral and non-viral delivery vehicles and programmable genetic payloads, which are then experimentally validated and tested in vivo to identify candidates with the properties required for therapeutic development. \r\n\r\nThrough this integrated approach, WhiteLab Genomics is building a portfolio of validated bio-assets that it advances independently and with pharmaceutical and biotechnology partners developing the next generation of genomic medicines.\r\n\r\nFounded in 2019, WhiteLab works with industrial and academic partners including Sanofi, Cytiva, the University of Massachusetts, the Vision Institute in Paris and the Paris Brain Institute, alongside additional collaborations with biotechnology companies. The company is part of Y-Combinator, Station F and Future4Care and is a French Tech 2030 laureate.\r\n\r\nWhiteLab Genomics operates from Paris, Boston, and Montreal.", "one_liner": "Unleash the potential of genomic medicine using AI", "team_size": 52, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1644584538, "tags": [ "Gene Therapy", "Cell Therapy", "Genomics", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "France", "Europe", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/whitelab-genomics", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/whitelab-genomics.json", "year_founded": 2019, "city": "Paris", "country": "FR", "linkedin_url": "https://www.linkedin.com/company/whitelabgenomics/", "x_url": "https://twitter.com/whitelabg?lang=en", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ba69864f65b85fda9f05c3280bc3b39f42500013.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": [ { "user_id": 818146, "full_name": "David Delbourgo", "title": "Founder", "founder_bio": "23 y of experience in healthcare innovation with multiple executive roles in Business Development, Marketing and Commercial Operations.\nFormer GE Healthcare. MBA Chicago Booth, Biomedical Engineer MS University of Compiègne, France.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/daviddelbourgo/", "x_url": "https://twitter.com/DavidDelBourgo", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c81c5cf739a324b1b2c85449e1553e6b4e509e6b.jpg" }, { "user_id": 847212, "full_name": "Julien Cottineau", "title": "Founder", "founder_bio": "Senior application scientist at Genomic Vision, leading provider of genetic devices and platform\nResearch fellow at Rockefeller Univ & Necker Hospital on human complex immune deficiencies in J-L Casanova’s lab\nPhD in genetics, molecular biology & immunology at Université Pierre & Marie Curie\u000bWith Honors\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/julien-cottineau-8a8b65112/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e08d2afde8df9bb0f1975eb86f59b18a4ac2afa3.jpg" } ], "jobs": [], "news": [ { "title": "How WhiteLab Genomics is using AI to aid gene and cell therapy development • TechCrunch", "url": "https://techcrunch.com/2022/09/12/how-whitelab-genomics-is-using-ai-to-aid-gene-and-cell-therapy-development/", "date": "Sep 12, 2022" } ], "launches": [] }, { "id": 26912, "name": "Quinn", "slug": "quinn", "former_names": [ "iollo" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bbcdfea1d8de2241298588710c23af6fc6a61bf6.png", "website": "https://www.quinnbio.com", "all_locations": "San Francisco, CA, USA", "long_description": "Quinn is your biopharma team. From molecule design to portfolio strategy, Quinn delivers comprehensive reports and supporting analyses, models and designs in 3–5 days instead of months.", "one_liner": "Your biopharma team, from molecule design to portfolio strategy", "team_size": 5, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1656567327, "tags": [ "Generative AI", "Biotech", "Enterprise", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 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": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/quinn", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/quinn.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/quinnbio/", "x_url": "https://twitter.com/iollohealth", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bbcdfea1d8de2241298588710c23af6fc6a61bf6.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 822976, "full_name": "Daniel Gomari", "title": "Founder/CEO", "founder_bio": "Daniel Gomari is the founder and CEO of Quinn. A Stanford postdoc and PhD in Computational Biology, his work spanned ML for drug repurposing across cancer and metabolic disease, and multi-omics studies on aging and lifespan extension. He built Quinn after watching the pharma industry make billion-dollar R&D decisions on intuition and slide decks instead of computational evidence.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/daniel-gomari/", "x_url": "https://twitter.com/daniel_gomari", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fb9dfbccdfd74d1e389c4c84aa5cd5f5664ed3ae.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": 27671, "name": "Miracle", "slug": "miracle", "former_names": [ "Miracle Health" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/708f104e98cf6195b515828cadc85b8d47cfc8d4.png", "website": "http://www.miracleml.com", "all_locations": "San Francisco, CA, USA", "long_description": "Miracle is building the control room for clinical trials. Today, biotech companies rely on fragmented software systems, spreadsheet trackers, and manual processes to manage complex clinical trials. Miracle integrates and consolidates data from disparate sources into a single platform, giving teams real-time visibility, helping them identify risks early, and streamlining workflows to make faster, better decisions.\r\n\r\nBy unifying clinical trial data, we're also laying the foundation for AI-powered analytics and automation.\r\n\r\nOur mission is to modernize how clinical trials are run so new treatments can reach patients faster.", "one_liner": "Accelerate clinical trials with real-time, actionable insights", "team_size": 7, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1677015940, "tags": [ "SaaS", "B2B", "Biotech", "Healthcare" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "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/miracle", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/miracle.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/miracleml/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/708f104e98cf6195b515828cadc85b8d47cfc8d4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/74b4283cb1ebc054eb4fa6c4a1481bd2f87c97af.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/2d3a0199b0e90622eba48919390f5831419408d4.png" ], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 985589, "full_name": "Jin Kim", "title": "Founder", "founder_bio": "Founder & CEO of Miracle | Former US Archery Team | MIT", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jinkimx10/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e97bf55bc790777059bb0e762555118288873f5b.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 69664, "title": "Miracle - Speeding up clinical trials 💊", "tagline": "Speeding up clinical trials by connecting patient recruitment sources", "body": "**TL;DR** Miracle helps biotech and pharma companies accelerate clinical trials by connecting all patient recruitment data sources. Each day of a clinical trial that Miracle’s software can help shave off is worth on average $2M in additional patent-covered revenue opportunity.\n\nHi everyone! My name is Jin, and I’m the Founder & CEO of Miracle. When I was competing for medals as a former athlete on the USA Archery Team, I used to aim my bow and arrow down at a target 70 meters away. Today, I have my eyes set on improving healthcare by bringing science to life and into hands of patients more quickly.\n\n![uploaded image](/media/?type=post&id=69664&key=user_uploads/985589/5efd5b1c-4740-45b5-88b5-9824dd2ce30b)\n\n### **The Problem**\n\nBiotech and pharmaceutical companies (“sponsors”) must run clinical trials (phase 1-3) in order to get FDA approval to market their drug. The **average cost to bring a new drug to market is >$2B**, and clinical trials can take up **7-10 years** of a 20-year patent window. Over **80% of clinical trials are delayed** from patient recruitment, but vendors and clinics do not play along nor provide the transparency necessary for sponsors to account for ROI and, ultimately, runway.\n\nToday, Clinical Operations teams within biotech companies spend **15% of their time manually compiling information** from multiple vendor portals and creating visualizations to report up to management and the board on the study progress – all of which take valuable time that they could be using to focus on improving the study operations.\n\nHowever, the bigger problem is that without transparent consolidated reporting, **they have no choice but to throw money at vendors and research sites** in hopes of recruiting patients quickly. They have a hard time optimizing the millions of dollars spent each year in a way that would actually expedite their trial.\n\n### **The Solution**\n\nMiracle is a **one-stop shop** for biotech/pharma to fully automate and consolidate their clinical trial reporting needs. We integrate with their vendors and research sites to **eliminate time wasted** on aggregating metrics and compiling reports, **free up their time** to focus on patient recruitment, and make it possible to **optimize ROI on vendors and sites** that are meaningfully speeding up the trial.\n\n### Examples from Miracle\n\n![uploaded image](/media/?type=post&id=69664&key=user_uploads/985589/3de6c2ca-0f94-45de-97d7-a781f5228494)\n\n![uploaded image](/media/?type=post&id=69664&key=user_uploads/985589/03be693f-8201-4534-aee3-050853ee439a)\n\n### Our Ask\n\n* If you’re at a biotech company running a clinical trial, we’d love to chat with you.\n* Any intros to biotech/pharma reps (especially in Clinical Operations) would be greatly appreciated.", "url": "https://www.ycombinator.com/launches/I7c-miracle-speeding-up-clinical-trials", "votes": 60, "created_at": "2023-02-24T21:32:28.618Z" } ] }, { "id": 27696, "name": "Novelcare", "slug": "novelcare", "former_names": [ "Recomp", "Resync", "Workpage", "Resync", "SelectIQ" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7c996f3a2f99cc3139f3821e758390c21efe8183.png", "website": "https://www.novelcare.com", "all_locations": "New York City, NY, USA; Remote", "long_description": "Novelcare addresses the primary bottleneck in drug development: recruiting patients into clinical trials.\r\n\r\nWe analyze electronic health records to help doctors find clinical trials for their patients, and to help trial sites find patients for their studies. 72% of doctors want to inform their patients of clinical trials, but only 5% do. We're on a mission to close that gap, ultimately bringing safer, more effective drugs to the patients who need them.", "one_liner": "Empowering every doctor and every patient to participate in research", "team_size": 5, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1677470068, "tags": [ "Health Tech", "B2B", "Healthcare", "Enterprise Software", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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 the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "The pace of progress in modern medicine is currently controlled by one thing: how quickly can you fill a clinical trial?---There is no shortage of novel molecules, there is no shortage of petri dish experiments, and there is no shortage of incentive to bring a drug to market. The bottleneck is clinical trials, and the bottleneck on almost every clinical trial is patient recruitment.---**Most trial recruitment tools are sugar on top of Facebook Ads. SelectIQ is instead helping patients learn about clinical trials the way they overwhelmingly prefer to: from their doctor.*** \\>70% of doctors want to tell their patients about clinical trials* <5% of doctors have ever referred a patient to a clinical trialWhy the gap? Doctors are notoriously overworked, experiencing record levels of burnout, and get to spend just a few minutes with each patient. Understanding which trials are available and whether their patient meets the complex eligibility criteria is a tall order. SelectIQ removes this burden from healthcare providers.---**We’re building a Provider Activation Platform that enables trial sites to build, maintain, and leverage a network of referring healthcare providers**, ensuring every patient is aware of every treatment option available to them. The first piece is automating _chart review_, the process by which a medical professional reads the patient’s record to assess whether they’re worth contacting about a study." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "If we succeed, we will dramatically accelerate the pace and reduce the cost-risk profile of drug development. Our loved ones will have better treatment options available when they inevitably need them." } ], "url": "https://www.ycombinator.com/companies/novelcare", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/novelcare.json", "year_founded": 2022, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/selectiq-ai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7c996f3a2f99cc3139f3821e758390c21efe8183.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 1159854, "full_name": "Ethan Bond", "title": "Founder", "founder_bio": "Ethan is the co-founder and CEO of SelectIQ. Prior to SelectIQ, Ethan was a product strategist at Palantir where he led product development into multiple new verticals, most recently helping to build Palantir's nascent life sciences division. Ethan is a product designer and software engineer by training, having *almost* graduated from Rensselaer Polytechnic Institute.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ethanbond/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8998c58707458dabdd6027469934990fc1c16d20.jpg" }, { "user_id": 90470, "full_name": "Jason Marmon", "title": "Founder", "founder_bio": "Jason is the co-founder and CTO of SelectIQ.\n\nPrior to SelectIQ, Jason worked at Palantir on life sciences software and led Palantir's Data Governance product team. Previously, he co-founded Skylight, an NYC real estate app acquired by Common Living. He's also a 2015 Thiel Fellow.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jtmarmon/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/39e84ac411a6dcadada52846f52ca3c9fe022dd9.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "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": 28112, "name": "Persist AI", "slug": "persist-ai", "former_names": [ "Persist AI", "Persist AI Formulations Corp" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/eeb1e38a6ca6e6061c101deae73f22c28c142596.png", "website": "http://www.persist-ai.com", "all_locations": "Sacramento, CA, USA", "long_description": "It takes 5 years for pharma to develop long lasting drug injections for chronic diseases like cancer and diabetes. Persist uses AI-driven automation to reduce formulation development time down to 2 years, a ~50% reduction.", "one_liner": "Developing long-lasting drug formulations 50% faster", "team_size": 6, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1671420729, "tags": [ "Machine Learning", "Robotics", "Microfluidics", "Nanotechnology", "Therapeutics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": true, "demo_day_video_public": true, "app_answers": [ { "question": "Describe what your company does in 50 characters or less.\n", "answer": "Help pharma develop slow release drugs faster" }, { "question": "What is your company going to make? Please describe your product and what it does or will do.\n", "answer": "For a pharma company to develop a sustained-release (slow release) drug formulation takes about 3-5 years and $10M or more. This is because there are a lot of variables and scientists' intuition (aka guesswork) needed to arrive at the final formulation solution.\r\n\r\nWe will provide a service to pharma companies to develop their formulations for them 50% faster and cheaper than current methods. To do this we will screen formulations in high throughput using automation and train ML which can make better predictions based on data (rather than guesses).\r\n\r\nThis will enable pharma companies to get drugs to market faster and earn revenue sooner." } ], "question_answers": null, "url": "https://www.ycombinator.com/companies/persist-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/persist-ai.json", "year_founded": 2022, "city": "Sacramento", "country": "US", "linkedin_url": "https://www.linkedin.com/company/persist-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/eeb1e38a6ca6e6061c101deae73f22c28c142596.png", "app_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/bf-public_profile/28167/app_video.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQC4NIECAOMAGS7ZB%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022712Z&X-Amz-Expires=60&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHEaCXVzLXdlc3QtMiJHMEUCIG5%2FEuPdHreXKRdGLYlyC2AjaYHPccvwucNqtFtDJEpNAiEAs4s5lSDOcogMQrwGhSKvVJXgimMvlIc1KK%2Fa7hojYD4q5QMIOhAAGgwwMDYyMDE4MTEwNzIiDMPa%2BQzgV8CSsX3XwyrCA9yWuwhv7VPDGJqPM%2BDoevlHz47k5lLRdgsGITLyqtSgmLV07z1Y86XDlb4dZMfcWmY5u5FLutDaUy2PMJ92q6vIq41n2jI5tC6SNSed8Qx%2Frdr9Wotko6WcUJWENt0Rs1K4pKZ8ZHCjH22BoWJkWLaTQ8fDKsmgWu0C8NMhnc8i44rKtbZLOvQwKZIlI546tkfyyliSiXPWdZ4XhGxEegateXp4TnM4yWfebpv22hrRHRotmFYX8ANU3B8yiEQL9yrU1y10N8c42JlHR%2FXHZNxaFQqX6eENp2aa2yewya2kPgdiQ3efnk5d0LiuPYxk7ZBa6uOug4fCBPHurDSGZqOlYX9D4h6PAMyUN2kZw2IcUR9QWKM2BmEsGorqokH5Oe34gs0DmnOGrGbhANOh9LKlKDu%2B6Pc4U%2F8L3%2Bmxug0q%2B3JBUGE7Wf6ySj%2B%2BbW8eOteynPrtIEUsD0pcfnSFNzQepsmnoTP5xc7qG%2FEn4MNWO3TviPmcIdg9qyW7bwOWKSyly4BuerWbZWVxIVolnUdqKu2PxDrgnb41QrRtObgKLlbhdehgPlqhIVqZ1cWH9s2uGHoiWlN5FRY8vYYodKzLGjDn%2FdrWBjqlAdXY8WsF5AC2X79ObZ%2FmZD1gjSCNFddCxk1e7RhQVznesCZJDMryJDWqJGSqI7lLEk62aPqOUbquB9Ghba%2BJXI%2FUV%2BHgkJZAPy7wzbnv8vLB8WccujudilaH29xgpqopxweKHuQzlcRMMeq0SZHCo3PizMnbhtLrqB%2BiK9IdfwMp8c15fBnQEe2RAGCsOWYNCrhsPiR6yT5V9698E5KnRPYRfOeFQw%3D%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=c66d7d91c1532ade147f60b6506c8084f0b87596e1d90190def0886579506001", "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/60/1680881229067-tn5frveent9-fbed84ada9379d7720a00b6d13e1f184/ba402ea2758b4d68f29d058ba99cd408.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022712Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=69e456b3a0e858aa28c02639034baef60514627411b140d9fa05f425a7d41c39", "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 1094851, "full_name": "Karthik Raman", "title": "Founder", "founder_bio": "When mom got brain cancer, I used to take her for her \"infusions\". \n\nFor 4 hours, we would sit there while drug was being infused into her veins and she had horrible nausea and hair loss.\n\nI built Persist to eradicate this horrible experience for future patients. \n\nWe package drugs so that patients take 1 injection per month or year, without an infusion. This lowers the overall dose the patient receives (reducing side-effects), improves compliance, and quality of life. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/karthik-raman-scientist/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ecc2a524e445309696a536435506c40bc98010ac.jpg" }, { "user_id": 1114143, "full_name": "Christopher Shelner", "title": "Founder", "founder_bio": "Career program manager, first time founder. Focus has been in electronics, automation, and manufacturing. Super excited to be part of the YC batch! ", "is_active": true, "linkedin_url": "http://www.linkedin.com/in/christophershelner/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/25ae4aac51df1a910d1efc90c169f83830fc6913.jpg" } ], "jobs": [], "news": [ { "title": "Beyond the $12M: Revolutionizing Drug Formulation Development at Persist AI", "url": "https://www.persist-ai.com/post/beyond-the-12m-revolutionizing-drug-formulation-development-at-persist-ai", "date": "May 19, 2025" }, { "title": "Persist AI Raises $4M in Seed Financing to Revolutionize Drug Formulation ", "url": "https://www.persist-ai.com/post/persist-ai-raises-4m-in-seed-financing-to-revolutionize-drug-formulation", "date": "Aug 04, 2023" } ], "launches": [ { "id": 69239, "title": "Persist - We build long-lasting drugs 50% faster 💊", "tagline": "Drug formulations made by robotics and designed by AI", "body": "### **TLDR**\n\nPersist helps pharma make drug injections that last up to 1 year. \n\nUsing robotics & AI, we reduce a 5-year drug formulation process to \\~2 years.\n\n**Contact Us (**[**founders@persist-ai.com**](mailto:founders@persist-ai.com)**) for a case-study formulation**\n\nTo make injections we encapsulate drugs inside injectable microspheres made with FDA-approved materials.\n\n![uploaded image](/media/?type=post&id=69239&key=user_uploads/1094851/19542a20-d5f0-4ccd-9450-d6b8427a6b94)\n\n**_Left:_** _Image of a \\~37 um microsphere made by our robotics_\n\n**_Right:_** _A Schematic showing how encapsulated drugs are released over 1 year so patients don’t take as many injections._\n\n### 🤔**THE PROBLEM**\n\nFor chronic diseases, most patients prefer 1 injection/yr rather than multiple injections per week.\n\nHowever, making a long-lasting injection takes a lot of trial and error and the time between iterations is long.\n\nKey challenges include:\n\n* **Using manual work & intuition is slow**: Typical teams manually mix and match chemicals to optimize the amount of drug that’s loaded into the spheres and the rate of release of drug\n* **Trial and error is cumbersome:** Because changing one variable may have multiple effects \n* **Scale-up is unpredictable:** Even if something works at a small scale, batch-to-batch consistency during scale-up is uncertain because production is done in large reactors\n\n### 💡**OUR SOLUTION**\n\nWe utilize custom robotics and AI to solve the problems with building microspheres. \n\n* **We automatically mix & optimize formulations:** We use a closed-loop robotics + AI approach so there’s minimal manual work\n* **We reduce trial & error with AI predictions:** We generate 50x more data than most formulation teams and use that data to train AI. This unique data enables us to make better predictions than others.\n* **We build small batches, faster:** So we can QC batches more frequently and scale up predictably\n\n### ❤️**OUR ASKS**\n\nIntroduce us to friends who work in Pharma. Email [founders@persist-ai.com](mailto:founders@persist-ai.com) \n\n“Follow” us [on LinkedIn](https://www.linkedin.com/company/persist-ai/)", "url": "https://www.ycombinator.com/launches/I0l-persist-we-build-long-lasting-drugs-50-faster", "votes": 36, "created_at": "2023-02-13T16:43:58.890Z" } ] }, { "id": 28158, "name": "Adventris Pharmaceuticals", "slug": "adventris-pharmaceuticals", "former_names": [ "Elimivax" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5c8bb0a987c29df94410bb9cc6ab7db9eb0d6478.png", "website": "https://adventris.com", "all_locations": "Baltimore, MD, USA", "long_description": "Adventris makes cancer vaccines. Our platform solves the problem of targeting poorly immunogenic oncogenes. Our first \"off-the-shelf\" vaccine targets KRAS, the most common cancer oncogene. In the long run, we envision a world where every adult receives our pan-cancer vaccines annually – preventing the majority of cancer deaths.\r\n\r\nIf you are interested in learning more about our approach to treat and prevent cancer, please reach out to us at contact@adventris.com", "one_liner": "Adventris makes cancer vaccines", "team_size": 7, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1675190671, "tags": [ "Biotech", "Healthcare", "Therapeutics", "Drug discovery", "Oncology" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "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/adventris-pharmaceuticals", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/adventris-pharmaceuticals.json", "year_founded": 2022, "city": "Baltimore", "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/5c8bb0a987c29df94410bb9cc6ab7db9eb0d6478.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 1145057, "full_name": "Jen Herbach", "title": "Founder", "founder_bio": "Jen Herbach is the CEO of Adventris Pharmaceuticals. She has more than a decade of biotech industry experience bringing novel oncology drugs to market, across companies including Onyx/Amgen, InterMune/Genentech, Exelixis, and Amunix. She holds an MBA from Harvard Business School and a Masters from the University of Cambridge.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jennylesser/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a4adf9402b75db976cbf8cf9bc9daa3538d621d2.jpg" }, { "user_id": 1157162, "full_name": "William Collis", "title": "Founder", "founder_bio": "William Collis is a serial entrepreneur with multiple exits, most recently the data platform Genji. His research has been published in Harvard Business review and featured as a TED talk with over 1.5M views. He holds an MBA from Harvard Business School where he was named a Baker Scholar. He is lifelong friends with his co-founders Jen Herbach and Mark Yarchoan, since meeting as roommates at Amherst College.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/william-collis-145a2511/", "x_url": "https://twitter.com/William_Collis", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b5c77e81b24d9b47c77c2c401669aded99074923.jpg" }, { "user_id": 1163176, "full_name": "Mark Yarchoan", "title": "Founder", "founder_bio": "Mark Yarchoan, M.D, is an Associate Professor of Medical Oncology at Johns Hopkins. Dr. Yarchoan runs an NCI-funded laboratory focused on developing novel immunotherapies, with a focus on cancer vaccines. He has led multiple international clinical trials of immunotherapy agents including neoantigen-targeted cancer vaccines. Dr. Yarchoan has received multiple honors, including the ASCO Young Investigator Award and the ASCO Career Development Award.", "is_active": true, "linkedin_url": "", "x_url": "https://twitter.com/markyarchoan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/57e4f209a83f48b812a625fa97dad9138e582500.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 68893, "title": "Adventris Pharmaceuticals - We make cancer vaccines!", "tagline": "By teaching our body’s immune system to recognize and kill the mutations that cause cancer we can eliminate the majority of all cancer deaths.", "body": "**ABOUT ADVENTRIS**\n\nAdventris makes cancer vaccines. By teaching our body’s immune system to recognize and kill the mutations that cause cancer, we can eliminate the majority of all cancer deaths.\n\n**OUR SCIENCE**\n\nA normal cell turns into cancer when it develops a mutation that makes it grow aggressively. Surprisingly, a small number of these mutations account for the vast majority of cancer deaths. The immune system usually doesn’t recognize these mutations, but our technology platform enables us to train the immune system to recognize and attack these cancers. Our first product is a vaccine for the treatment of lung cancer that targets the KRAS mutation, the most common mutation in cancer, accounting for about 25-30% of all cancer deaths. In the long run, we envision a world where every adult receives our vaccines annually – preventing the majority of cancer deaths.\n\n**OUR TEAM** \n\nOur team includes some of the leading cancer vaccine experts in the world including: our Chief Medical Officer, Dr. Mark Yarchoan (principal investigator of multiple international cancer vaccine clinical trials) and our Scientific Co-Founder, Dr. Liz Jaffee. Our team is led by our Chief Executive Officer, Jen Herbach, who has more than a decade of biotech industry experience including with Amunix (the second largest oncology pre-clinical stage acquisition).\n\n![uploaded image](/media/?type=post&id=68893&key=user_uploads/1145057/259fde86-331d-4477-83f2-f7f6e5461adf)\n\n**INTERESTED IN LEARNING MORE** \n\nIf you are interested in learning more about our approach to treat and prevent cancer, please reach out to us at [contact@adventrispharma.com](mailto:contact@adventrispharma.com)", "url": "https://www.ycombinator.com/launches/HvB-adventris-pharmaceuticals-we-make-cancer-vaccines", "votes": 61, "created_at": "2023-01-31T19:18:22.720Z" } ] }, { "id": 28159, "name": "Invitris", "slug": "invitris", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b31c6e14e61e0a7e292383c88d7094914a492565.png", "website": "https://invitris.com/", "all_locations": "Munich, BY, Germany", "long_description": "Invitris radically simplifies how we create novel biological drugs. Our patent-pending technology platform turns DNA into synthetic proteins and was recently featured in the Science magazine. It makes creating new drugs >10,000x more efficient at <100x lower costs of material.\r\n\r\nOur killer application is enabling the newest generation of synthetic bacteriophages to combat antibiotic-resistant infections, which are threatening to cause more deaths than all cancers combined. ", "one_liner": "Creating new protein-based drugs at scale", "team_size": 6, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1678475135, "tags": [ "Biotech", "Nanotechnology", "Drug discovery" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "Germany", "Europe", "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/invitris", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/invitris.json", "year_founded": 2022, "city": "Munich", "country": "DE", "linkedin_url": "https://www.linkedin.com/company/invitris/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b31c6e14e61e0a7e292383c88d7094914a492565.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1163538, "full_name": "Patrick Grossmann", "title": "Founder", "founder_bio": "My hearts are entrepreneurship & science, originally trained as bioinformatician at the Harvard Medical School and ETH Zurich amongst other schools. I earned a PhD and MBA, and highly enjoy opportunities to improve the world with science-based products. I co-founded Invitris to radically simplify drug development to stop antibiotic-resistance, which has the potential to become the dominating health-economic challenge in this century.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/patrickgrossmannbioinformatics/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7fd393675a42df3ca1abac5c1131e3248a325274.jpg" }, { "user_id": 1195343, "full_name": "Kilian Vogele", "title": "Founder", "founder_bio": "Kilian, the CTO and co-founder of Invitris, is responsible for driving innovation and development within the company. With a PhD in synthetic biology from TUM and a track record of 5 patents, his expertise has been instrumental in creating Invitris' groundbreaking technology.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kilian-vogele-21a321183/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4cb832abbb78ced414d19e28f010a711ebeb3ece.jpg" } ], "jobs": [], "news": [ { "title": "Innovation and Start-up Center Biotechnology (IZB) welcomes Invitris - Innovations- und Gründerzentrum Biotechnologie IZB", "url": "https://www.izb-online.de/en/media-library/press-release/press-release-izb-martinsried/innovation-and-start-up-center-biotechnology-izb-welcomes-invitris/", "date": "Feb 28, 2023" }, { "title": " moma future: Viren gegen Bakterien - ZDFheute", "url": "https://www.zdf.de/nachrichten/zdf-morgenmagazin/moma-future-viren-gegen-bakterien-100.html", "date": "Feb 06, 2023" }, { "title": "Bacteriophages the Modern Way | Science | AAAS", "url": "https://www.science.org/content/blog-post/bacteriophages-modern-way", "date": "Aug 17, 2022" } ], "launches": [ { "id": 70166, "title": "INVITRIS - Simplifying drug development & manufacturing", "tagline": "Our biotech platform creates the newest drugs in shortest time", "body": "### **Tl;dr**\n\nAt Invitris we're creating **protein-based drugs**, such as the next generation of antibiotics. To do this at scale, we created a platform called **Phactory**, that can turn DNA into synthetic proteins. Phactory makes the development of novel protein-based drugs 10,000x more efficient and cuts down the material costs by 100x.\\\n—\n\nHi everyone! This is [**Kilian** ](https://www.linkedin.com/in/kilian-vogele-21a321183/)and [**Patrick**](https://www.linkedin.com/in/patrickgrossmannbioinformatics/) from INVITRIS, a Munich-based life science company.\n\n![uploaded image](/media/?type=post&id=70166&key=user_uploads/89898/df875741-b00f-4ba8-b56f-48cee390fbf1)\n\n# **Protein-based drugs are powerful but expensive**\n\nWhile only 2% of all treatments are based on protein-based drugs, these drugs generate 40% of the total drug revenue. One of the reasons why they are so expensive is that you need living cells, like bacteria, to produce them. **Handling living cells is error-prone** and **scales terribly** because you have to establish new processes for every new drug.\n\n# **One platform to synthesize them all**\n\nOur universal biotech platform, **Phactory**, takes DNA and uses it to synthesize proteins in a single step. It mimics the molecular machinery you find in living cells but can be stored in conventional vessels.\n\n![uploaded image](/media/?type=post&id=70166&key=user_uploads/1163538/e720da18-e212-42d5-9a13-a5e53fbecc80)\n\nThis platform is based on Kilian’s groundbreaking research during his PhD, which was honored in the famous magazine [**Science** ](https://www.science.org/content/blog-post/bacteriophages-modern-way)and was also featured on [**German national TV**](https://www.zdf.de/nachrichten/zdf-morgenmagazin/moma-future-viren-gegen-bakterien-100.html)!\n\nOur primary focus area is **bacteriophages** - natural viruses that cannot attack humans but bacteria, even when those have become resistant to antibiotics. We are therefore tackling a problem that is expected to cause 25% more deaths per year than all cancers combined in the next decades: [multidrug-resistant infections](https://www.stopamr.eu/blog/new-studies/no-more-wake-up-calls-amr-is-now-the-leading-cause-of-death-worldwide/).\n\n# **We believe in collaboration and partnering!**\n\nBuilding new protein-based products is hard. We can help each other.\n\n* We are constantly looking for **biopharmaceutical partners** seeking innovation!\n* Intros to companies developing antibacterial products in animal health or agriculture are also welcome!\n* If you **struggle with protein synthesis** in general - we can help!\n* Know anyone who is experienced in scaling cell-free expression under GMP? Reach out!\n\nAny outreach is highly appreciated:\n\n![uploaded image](/media/?type=post&id=70166&key=user_uploads/1163538/d016e457-3a55-4651-a7f6-6308d85772cb)\n\n", "url": "https://www.ycombinator.com/launches/IFi-invitris-simplifying-drug-development-manufacturing", "votes": 45, "created_at": "2023-03-10T22:48:50.259Z" } ] }, { "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": 29128, "name": "Olio Labs", "slug": "olio-labs", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6cb70c7b1c00797475c860562acfdb3a164629b8.png", "website": "https://oliolabs.co/", "all_locations": "Remote", "long_description": "Olio labs uses AI to develop combination therapeutics that consider the thousands of interacting proteins in your body rather than targeting just one or two. Their lead combinations target obesity and are more effective with fewer side effects than Ozempic, the fastest growing drug of all time. Their custom ML built from real-world expertise and cutting edge AI searches trillions of combinations to find the perfect one.", "one_liner": "Combination therapeutics for the world's toughest diseases", "team_size": 5, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1689797916, "tags": [], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/olio-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/olio-labs.json", "year_founded": 2023, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/olio-labs/?viewAsMember=true", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Olio-Labs/olio", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6cb70c7b1c00797475c860562acfdb3a164629b8.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 1288444, "full_name": "David Tingley", "title": "Founder", "founder_bio": " David has over 13 years of experience as a systems neuroscientist and biologist. His interest in systems biology led him to earn a PhD in Neuroscience at New York University and complete postdoctoral research at Harvard. During this time, he published more than 15 scientific articles and uncovered a link between oscillations in brain activity and glucose metabolism in the body (Tingley et al., Nature 2020). ", "is_active": true, "linkedin_url": "https://linkedin.com/in/david-tingley-0428b5229", "x_url": "https://twitter.com/davidwtingley", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2b90e3ec5d4fc7313dc5dec2bd305b0adc9ae7cd.jpg" }, { "user_id": 1357697, "full_name": "Tom Roseberry", "title": "Founder", "founder_bio": "Tom has over 15 years of industry experience leading teams and engineering technologies in aerospace, brain machine interfacing, synthetic biology and drug discovery/development. At Neuralink he built and co-led a group that developed a next-generation neural interface. At Loyal he built and led multiple teams as VP of Engineering, Computational Biology and Drug Development.", "is_active": true, "linkedin_url": "https://linkedin.com/in/tom-roseberry-a84501163", "x_url": "https://twitter.com/tomroseberry789", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a5f44d29c4554851b8be147f2a8f7d36f8f6a987.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 73765, "title": "Olio Labs - Combination therapeutics for the world's toughest diseases", "tagline": "Using AI to design better treatments for disease ", "body": "**In short**\n\nOlio labs uses AI to develop combination therapeutics that consider the thousands of interacting proteins in your body rather than targeting just one or two. \n\n**The problem**\n\nDrug discovery focuses on finding therapeutics that target one or two proteins. But proteins are found in many organs throughout the body, which results in side effects. \n\n![](https://lh3.googleusercontent.com/nCXuE0YFP47YFSREM_gvtYJdIdYEY4iqI-SvmYumTPcTycsPM0pa0KmgMrnUoTWmHuJ7PYW8OXC88mmqJU39vB2P_gVb11IK0Erikj9v4cL-9lIIlqsXnHoUbVGboDGr0pEBTV0ozSOTWiL6XjeUmw)\n\nCombinations of therapeutics allow for better targeting in the right places.\n\n![](https://lh4.googleusercontent.com/lkQf4Z1hE3SBtbiHzZLy_GakojBT9WD0HNEvFGnwzdHRMOLU7C1fju9LVOKrY8Zp0L14qhkSr9orPiFmBvFThYwpZsb1KYrkPbWtUYmV8UOCU-Kw5AnA5ZvFhkSb86abKwnQgjODJb6sVmb4j5VxQg)\n\nOptimizing a combination requires testing trillions of combinations, which is impossible _in vitro_ (cell culture) or _in vivo_ (rodents). \n\n**Solution**\n\nWe have developed a computational platform that allows rapid _in silico_ testing to find only the most promising combinations to move forward. \n\n![uploaded image](/media/?type=post&id=73765&key=user_uploads/1288444/02e216ba-c2be-4553-879f-0451a6c89120)\n\nWe have lead combinations that beat the currently available market-leading drugs. \n\n**Who are we?**\n\n![](https://lh4.googleusercontent.com/aAdMatFLBHE8DYk6SKxdxo5PXq8QZQPDYaxsIO6zoUylUVscOekD8f7pIyNhrs7D2fQiCJ99074X5owKeHxQqy9_vb5D2Eb1E9VQpeA2jGQ_gjku46dwsfojMtwJB75KvcofBgbZSmPqhmO1GHuybQ)\n\nDavid and Tom are both systems neuroscientists and worked together on the Computational Biology team at Loyal for Dogs.\n\nIn our PhDs we used research tools that allowed single neurons to be turned on or off in order to test their function. These tools are not available in humans, but is there another way to be this precise? Combinations of therapeutics make it possible.\n\nWe are a new type of scientist that has emerged over the past decade - as comfortable working at the bench as we are in the codebase. This means we have a full understanding of the problems and tools, from cell to model, with no disconnect between engineers and scientists. We are looking to automate every aspect of the drug discovery and development process and expand our platform into multiple indications. \n\n**Get in touch with us if you can help us find:**\n\n* **Connections** in mid to large **pharmaceutical companies**.\n* **Leads** on any developing **molecules** you find interesting that we can test out in our models. \n* **Builders** in the AI x Bio space who might resonate with our mission.\n\n… or if you just want to learn more!\n\n[founders@oliolabs.co](mailto:founders@oliolabs.co)", "url": "https://www.ycombinator.com/launches/JBl-olio-labs-combination-therapeutics-for-the-world-s-toughest-diseases", "votes": 29, "created_at": "2023-08-04T16:55:29.868Z" } ] }, { "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": 29749, "name": "Biocartesian", "slug": "biocartesian", "former_names": [ "coordna bio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/91093b7c8dbe1ed84a73aa4907b878fc5b0f52bf.png", "website": "https://www.biocartesian.com", "all_locations": "Philadelphia, PA, USA", "long_description": "Finding new cures requires seeing what and where the abnormal molecules are in a diseased tissue, but current tools see less than 1% of those molecules. Biocartesian combines microscopy and new chemistries to see 50X more, offering unprecedented insights into disease biology and new therapies.", "one_liner": "Converting biology into digital molecular maps", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1724915415, "tags": [ "Hard Tech", "B2B", "Biotech", "Diagnostics", "Drug discovery" ], "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" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/biocartesian", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/biocartesian.json", "year_founded": 2023, "city": "Philadelphia", "country": "US", "linkedin_url": "https://www.linkedin.com/company/biocartesian", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/91093b7c8dbe1ed84a73aa4907b878fc5b0f52bf.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1116949, "full_name": "Ian Dardani", "title": "Founder/CEO", "founder_bio": "Co-founder of Biocartesian. Microscopy + molecular tools + engineering. Previously Bioscience Enterprise at Cambridge, Bioengineering PhD at UPenn.", "is_active": true, "linkedin_url": "https://linkedin.com/in/ian-dardani", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6c967a8fcb81f0bfbca52fb800b5ba1f7eea275a.jpg" }, { "user_id": 1975436, "full_name": "Jess Li", "title": "Founder", "founder_bio": "Co-founder of Biocartesian. Genetics + epigenetics, next-gen sequencing. Previously PhD at UPenn, BD at imvaria.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jessxli/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8e804f1e103e4d7f9e0e7173849d9d26dbf96a17.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83575, "title": "Biocartesian - Converting biology into digital molecular maps", "tagline": "We use chemistry and microscopy to help scientists discover new drugs.", "body": "**Tl;dr**: Finding new cures requires knowing **what** and **where** the abnormal molecules are in a diseased tissue, but current tools see less than 1% of those molecules. [Biocartesian](https://www.biocartesian.com/) combines microscopy and new chemistries to see a comprehensive picture, offering unprecedented insights into disease biology and new therapeutic approaches.\n\n![uploaded image](/media/?type=post&id=83575&key=user_uploads/89898/ee18ecba-242d-4cb0-bb1b-d9ab6de69ddd)\n\n# **👩‍🔬 Team**\n\nWe’re [**Ian**](https://www.linkedin.com/in/ian-dardani) and [**Jess**](https://www.linkedin.com/in/jessxli/), two former UPenn PhDs on a mission to see every molecule in biological specimens. We’ve spent years advancing and troubleshooting the latest tools in biology, from DNA sequencing to microscopy, but even with these efforts, these tools revealed only a fraction of the underlying biology. We dreamed of a way for scientists to see everything (and fast) so they could reduce their guesswork and focus on the most promising therapeutic approaches.\n\n# ❌ **Problem**\n\nMost diseases remain a mystery because existing methods can see less than 1% of the molecules within a biological specimen. These methods can also take months for scientists to set up and often fail, wasting thousands of dollars per specimen and millions in failed drug programs.\n\n# 🔬 **Our approach**\n\nWe’re combining new chemistries and microscopy to see a full map of the molecules in biological specimens. Imagine if we could see into the \\~500 million existing tumor specimens to decode these cancers.\n\nTo use our technology, scientists simply ship their biological specimens to our laboratory, where we automate their conversion into digital molecular maps – saving them months of tedious data collection and troubleshooting.\n\n![uploaded image](/media/?type=post&id=83575&key=user_uploads/1116949/7deca4d9-cf29-45b4-852d-1fc471672b75)\n\nOur solution:\n\n* Enables scientists in any disease area to 50X their data collection\n* Saves scientists months of time\n* Lowers the cost of biological measurements\n* Expands access to bespoke assays developed by any scientist by integrating with our platform\n* Meets the growing demand for biological data as AI-led analysis supersedes the bottleneck of human-led analysis\n* Turns biology from 99% invisible to an interpretable problem\n\n# **🙏 Asks**\n\nIf you’re excited about this and would like to learn more, email us at:\n\n[**interested@biocartesian.com**](mailto:interested@biocartesian.com)\n\nor reach us at ", "url": "https://www.ycombinator.com/launches/Ljz-biocartesian-converting-biology-into-digital-molecular-maps", "votes": 265, "created_at": "2024-08-29T15:17:26.070Z" } ] }, { "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": 30970, "name": "Exonic", "slug": "exonic", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5cc27505c22c402a3b3d1ac7fac9315f0ab93577.png", "website": "https://exonic.ai", "all_locations": "San Francisco, CA, USA", "long_description": "We program DNA sequences to be context-aware, unlocking new kinds of genetic therapies.", "one_liner": "Exonic is an AI-native cell and gene therapeutics company. ", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1762663893, "tags": [], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 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": null, "url": "https://www.ycombinator.com/companies/exonic", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/exonic.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/exonicbio/", "x_url": "https://x.com/exonicai", "fb_url": "https://www.facebook.com/profile.php?id=61582621808913", "cb_url": "https://www.crunchbase.com/organization/exonic", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/5cc27505c22c402a3b3d1ac7fac9315f0ab93577.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": 2083214, "full_name": "Ben Brimacombe", "title": "Founder/CEO", "founder_bio": "Co-founded Free2Grow, one of the largest AI voice companies in the home services vertical today.\nCo-led Turnpoint contract ($1.4B revenue aggregator) at Free2Grow. \nColumbia Computer Science and Mathematics, 2020.\nPublished AI researcher.\nLed machine learning at Long Run Partners, an NYC mortgage bank.\nResearched ML at Numerai, the crowdsourced hedge fund ($500m+ AUM). ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/benjamin-brimacombe/", "x_url": "https://x.com/thebrimacombe", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/578f87186dce856bae8932a77e7f0925af73d890.jpg" } ], "jobs": [ { "id": 95156, "title": "Founding Scientist (Synthetic Genomics)", "url": "https://www.ycombinator.com/companies/exonic/jobs/TknRQEX-founding-scientist-synthetic-genomics", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Science", "role_type": "Biology", "salary_range": "$120K - $180K", "equity_range": "0.50% - 2.50%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Cell biology", "Molecular biology", "Wet lab", "Biology" ] } ], "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": 31108, "name": "Strand AI", "slug": "strand-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b3ab1b6b156ac5fcdb95c20ddc1b2ebfc47a69ae.png", "website": "https://strandai.com", "all_locations": "San Francisco, CA, USA", "long_description": "Strand AI develops foundation models to generate missing bio-data about patients. With this imputed data, pharmaceutical companies can select better patients for their drug trials and shave months from their drug launch timelines. We’ve already trained a multimodal foundation model that integrates spatial biology modalities, beating SOTA at a fraction of the cost.", "one_liner": "Multimodal foundation models to predict uncollected patient biology", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1769406494, "tags": [], "tags_highlighted": [], "top_company": false, "isHiring": false, "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/strand-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/strand-ai.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://linkedin.com/company/strand-ai/", "x_url": "https://x.com/strandaibio", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b3ab1b6b156ac5fcdb95c20ddc1b2ebfc47a69ae.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/e776762c9ced51e971d3ac1ad7301ad1b480ebc0.png" ], "primary_partner": { "name": "Jon Xu", "url": "https://www.ycombinator.com/people/jon-xu" }, "founders": [ { "user_id": 1489450, "full_name": "Yue Dai", "title": "Founder/CEO", "founder_bio": "Cofounder & CEO at Strand AI\nex-Pathos AI, Enable Medicine, Microsoft Research, Element AI.\nTrained foundation models for biology on the largest patient dataset in existence.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/yued/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bbe4ba85fab9f7bec638430f58741bc9e837217f.jpg" }, { "user_id": 3118673, "full_name": "Oded Falik", "title": "Founder/CTO", "founder_bio": "Cofounder & CTO at Strand AI\nEx-Enable Medicine\nBuilding foundation models to enrich biology data and improve patient outcomes.\n\nOded Falik, CTO, was the Tech Lead for Enable Medicine's spatial biology platform, scaling to 12B+ single-cell annotations and petabyte-scale pipelines. Programming since 8 years old, published first app on iOS app store at 11 years old.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/oded-falik/", "x_url": "https://x.com/oded_falik", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6f84197edfe09c88045fa06ae54b4651a871e6a4.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 98178, "title": "Strand AI - The Data Layer for Biology.", "tagline": "Strand AI builds foundation models to turn sparse clinical trial datasets into complete multimodal profiles.", "body": "We're Yue and Oded, the founders of Strand AI ([strandai.com](http://strandai.com)).\n\n## TL;DR\n\nStrand AI develops foundation models that generate missing bio-data about patients. With this imputed data, pharmaceutical companies can select better patients for their drug trials and shave months from their drug launch timelines. We’ve already trained a multimodal foundation model that integrates spatial biology modalities, beating SOTA, at a fraction of the cost.\n\n![uploaded image](/media/?type=post&id=98178&key=user_uploads/1489450/997163fb-e9dc-478b-a083-a1d128fd25af)\n\n## The Problem\n\nEvery year, $60-100B is invested into clinical trials that do not result in approved therapies. 9 out of 10 drug trials fail. These drugs work by targeting specific biological pathways, but not every patient has the same pathways active. When a trial can't tell who has what, responders get diluted by non-responders, the effect washes out, and the trial fails. The key to selecting the right patients is multimodal data: genomics, RNA, proteins, imaging, clinical outcomes. But for any given patient, most of this data is missing. You might have their RNA expression but no pathology slides. Their genomics but not their clinical outcomes. The full picture is never there.\n\nYue saw this firsthand. Before starting Strand AI, he was building foundation models on the largest patient dataset in existence, working directly with the founders of Tempus AI. Whether predicting patient drug response, modeling disease progression, or finding new mechanisms of action, every task hit the same wall. Even with the largest dataset in existence, there simply wasn't enough to make good predictions.\n\nThe easy answer is \"collect more data.\" But multimodal patient data is expensive, invasive, and often impossible to get. Patients drop out of trials, skip evaluations, or are part of rare populations where the data simply doesn't exist.\n\n## Our Insight\n\nThe good news is that there's enough paired data across modalities that the missing biology can be intelligently and accurately predicted from what's already there. Strand AI builds cross-modal prediction models that take what you already have and predict what you don't.\n\nWe can efficiently evaluate which modalities we're able to predict accurately for a given dataset, so teams know exactly where we can fill gaps and when new measurements are needed. This lets pharma and biotech teams take their incomplete clinical cohorts and unlock precision that would otherwise be locked behind missing data.\n\n\n\n## What We've Built\n\nWe recently trained from the ground up a foundation model that predicts spatial proteomics from routine H&E staining. It beats state-of-the-art and we did it in under 6 weeks, at a fraction of the cost of comparable efforts. We were able to achieve this due to our deep, hands-on experience with both the data and the infrastructure. We’ll be deploying this model at scale for pharmaceutical and biotech companies to enrich their patient data.\n\n## About Us\n\nBefore Strand AI, we worked shoulder-to-shoulder at Enable Medicine, where we built the platform for petabyte-scale multimodal spatial biology data — single-cell resolution imaging, spatial transcriptomics, spatial proteomics, and clinical metadata — all linked at the patient level. We indexed, analyzed, and served the largest spatial biology datasets in existence. That work gave us an unusually deep understanding of what multimodal biological data actually looks like at scale — what's missing, what's noisy, and what it takes to make it useful.\n\nOded has been programming since he was 8. At Enable Medicine, he led product to build the spatial biology platform end-to-end.\n\nYue previously worked at Yoshua Bengio's Element AI, Microsoft Research, and Pathos, a Tempus AI initiative, where he built foundation models on the largest multimodal patient dataset in existence. He's built and managed ML infrastructure at scale, including clusters of thousands of GPUs.\n\nWe know how to train large models efficiently, and it shows in our costs and timelines.\n\n## Our Ask\n\nWe're looking for introductions to decision makers at pharma and biotech companies running clinical trials. If you know someone who wants to find better biomarkers, stratify patients more precisely, or catch signals they're currently missing, we can help. When a trial fails, it's not just years and capital lost, it's patients who were counting on that treatment. Better data means better patient selection, and drugs that reach the people who need them.\n\nreach us at [founders@strandai.com](mailto:founders@strandai.com)", "url": "https://www.ycombinator.com/launches/PXW-strand-ai-the-data-layer-for-biology", "votes": 604, "created_at": "2026-02-25T18:26:57.743Z" } ] }, { "id": 31113, "name": "CellType", "slug": "celltype", "former_names": [ "Contextual Bio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7626449e8e50e3bcef406586024dca5da4b7918b.png", "website": "https://www.celltype.com", "all_locations": "New York City, NY, USA", "long_description": "CellType is building an agentic drug company. AI agents that run the full drug discovery pipeline on top of biological foundation models that simulate human biology. Our core technology, developed with Google DeepMind, has already discovered and validated a new cancer treatment signal. We’re working with Top 10 pharma.", "one_liner": "The agentic drug company. We simulate human biology.", "team_size": 5, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1771295425, "tags": [], "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/celltype", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/celltype.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/celltype/", "x_url": "https://x.com/celltypeinc", "fb_url": null, "cb_url": null, "github_url": "github.com/celltype", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7626449e8e50e3bcef406586024dca5da4b7918b.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": 2244977, "full_name": "David van Dijk", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO of CellType (W26). Yale Professor, 11,000+ citations. Built biological foundation models with Google. Building the agentic drug company.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/david-van-dijk-b440286/", "x_url": "https://x.com/david_van_dijk", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4aed0d9f833eb48d703f8cab3ca84261242287d9.jpg" } ], "jobs": [ { "id": 108489, "title": "Founding Research Scientist, Biological Foundation Models", "url": "https://www.ycombinator.com/companies/celltype/jobs/ynLk0Xr-founding-research-scientist-biological-foundation-models", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$150K - $250K", "equity_range": "0.50% - 2.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "Torch/PyTorch", "Machine Learning", "Deep Learning", "Computer Vision", "Bioinformatics", "Genomics", "Transformers", "Multimodal AI" ] } ], "news": [ { "title": "Sundar Pichai announces Google's AI milestone in cancer cure", "url": "https://etedge-insights.com/technology/artificial-intelligence/sundar-pichai-announces-google-deepmind-yale-breakthrough-in-cancer-therapy-using-ai/", "date": "Oct 16, 2025" }, { "title": "Google CEO Sundar Pichai Says C2S-Scale 27B Foundation Model Generated 'Novel Hypothesis About Cancer Cellular Behavior'", "url": "https://www.etvbharat.com/en/technology/sundar-pichai-enn25101506952", "date": "Oct 15, 2025" }, { "title": "Google’s Gemma AI model helps discover new potential cancer therapy pathway", "url": "https://blog.google/technology/ai/google-gemma-ai-cancer-therapy-discovery/", "date": "Oct 15, 2025" }, { "title": "Google’s AI Cracks a New Cancer Code", "url": "https://www.yahoo.com/news/articles/google-ai-cracks-cancer-code-203652400.html?guccounter=1", "date": "Oct 14, 2025" } ], "launches": [ { "id": 97885, "title": "CellType: The Agentic Drug Company", "tagline": "What takes big pharma years, we aim to do in weeks.", "body": "We're David and Ivan — we built this technology at Yale and are now turning it into a company.\n\nAI is fundamentally changing how software gets built. Drug discovery is next.\n\n# TL;DR\n\nWe're building an agentic drug company: **foundation models that learn human biology + AI agents that run the full discovery pipeline**. Instead of mice and cell lines, we simulate what will happen in patients — before you run the trial.\n\n![uploaded image](/media/?type=post&id=97885&key=user_uploads/1493577/d81c7718-831b-4ed3-8b76-c1f050d31193)\n\nOur core technology, Cell2Sentence ([ICML 2024 paper](https://proceedings.mlr.press/v235/levine24a.html) · [C2S-Scale paper](https://www.biorxiv.org/content/10.1101/2025.04.14.648850v4)), was [featured by Google](https://blog.google/technology/ai/google-gemma-ai-cancer-therapy-discovery/) and shared by **Google CEO Sundar Pichai** (7M+ views). It teaches LLMs to understand cellular biology, and we've already used it to **discover and validate a new cancer treatment signal**.\n\n![uploaded image](/media/?type=post&id=97885&key=user_uploads/1493577/0b848ed1-640a-4601-bd61-54ab7d22f4f1)\n\n# The Problem\n\n90% of drugs fail in clinical trials. Most of that failure traces back to one thing: our preclinical models don't match humans. Mice and cell lines don't represent what actually happens in patients, so teams spend years and billions optimizing against the wrong reality.\n\nAnd the process itself is broken: massive teams, manual workflows, siloed data, and painfully slow iteration. A typical drug takes 10+ years and $2B+ to develop. Most fail anyway.\n\n[https://www.youtube.com/watch?v=MBlSLzVBnH8](https://img.youtube.com/vi/MBlSLzVBnH8/0.jpg\\)%5C%5D\\(https://www.youtube.com/watch?v=MBlSLzVBnH8\\))\n\n# Our Approach: AI Agents + A Virtual Human\n\nWe're building a new type of drug company from scratch around AI.\n\n**The vision: AI agents do the work, humans steer the science.** Today, drug R&D means hundreds of scientists manually digging through literature, analyzing data, generating hypotheses, designing experiments, and coordinating with contract labs. We're replacing that with autonomous agents that execute end-to-end.\n\n**The technology:** we taught LLMs to understand biology. Our core insight is that biology has structure, like language — gene expression profiles are sentences, cellular states encode meaning, drug responses are transformations. We built Cell2Sentence to convert single-cell biology into a form that foundation models can learn from. Unlike protein-centric approaches (AlphaFold, Boltz, ESM), we model the whole biological system — cells, tissues, microenvironments, and drug-cell interactions. **Proteins are parts; we model the machine.**\n\n**The product:** a virtual human — a computational model of human biology you can test drugs on before touching a real patient. At every stage of development, you can ask \"what will happen in humans?\" and get an answer grounded in real human data, not proxies. Target discovery, toxicity prediction, translational prediction, patient stratification, virtual trials.\n\nOur work is published at ICML 2024.\n\n# Validation: We Discovered a New Cancer Treatment Signal\n\nWe didn't just build a model, we used it to find something real.\n\nWe screened 4,000+ drugs and predicted a novel hit: a drug that could help turn \"cold\" tumors \"hot\" (more visible to the immune system) — something that wasn't previously known. We then validated it in the lab — both in cell lines and in intact human tumor microenvironments that mirror what happens in patients. **The prediction held up.**\n\nThis is the kind of result that takes traditional R&D teams years.\n\n# Traction\n\nWe're **already working with Top 10 pharma** — and every deal has been **100% inbound**. They saw the work and reached out to us.\n\n# Our Team\n\nWe're two founders with deep expertise in biology + AI who invented the underlying technology together at Yale.\n\n**David van Dijk, PhD:** Yale Professor, 11,000+ citations, published in Cell, Nature, ICML, and NeurIPS, turned down Google to build CellType.\\\n**Ivan Vrkic:** co-built the core tech at Yale (published at ICML), built software to control CERN's Large Hadron Collider, and led large-scale foundation model training at a biotech company.\n\n# Our Ask\n\nIf you're a pharma or biotech team that wants to de-risk discovery and development — especially around translational and clinical prediction — we'd love to talk. We're also looking for data partners and exceptional bio and ML builders who want to help build an agentic drug company from scratch.\n\n[david@celltype.com](mailto:david@celltype.com)", "url": "https://www.ycombinator.com/launches/PSn-celltype-the-agentic-drug-company", "votes": 25, "created_at": "2026-02-17T13:24:49.230Z" } ] }, { "id": 31225, "name": "Ditto Biosciences", "slug": "ditto-biosciences", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0d5b227bb99dde80a75d7008740db6747b12af69.png", "website": "https://www.dittobio.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Ditto Bio discovers drugs for autoimmune diseases by harnessing parasite biology. Viruses, ticks, and worms have experimented over millions of years to create proteins that control the human immune system. We use AI to identify these proteins and engineer them into next-generation autoimmune therapies.\r\n\r\nOur team is three PhD scientists from UCSF, UCSD, and UC Berkeley who have worked together for 3+ years in biotech. We have deep expertise in parasite biology, computational biology, and software for AI-based protein structure prediction", "one_liner": "Evolutionary intelligence for autoimmune disease", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1770234545, "tags": [], "tags_highlighted": [], "top_company": false, "isHiring": false, "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/ditto-biosciences", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/ditto-biosciences.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "http://linkedin.com/company/dittobio", "x_url": "https://x.com/dittobio", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0d5b227bb99dde80a75d7008740db6747b12af69.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": 2915094, "full_name": "Dennis Sun", "title": "Founder/CEO", "founder_bio": "Scientist and operator transforming natural proteins into therapies.\n\nTrained in evolutionary and developmental biology at Harvard (AB) and UC Berkeley (PhD). Previously led software teams, product development, and scientific partnerships as Chief of Staff at a $500M biotech. Now building a platform to mine proteins from viruses, ticks, and worms that will become the next blockbuster drugs for autoimmune disease.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/dennis-sun-57740532/", "x_url": "https://x.com/mezarque", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4e21137f5ba86792982fc00ecc39f47c0227b44f.jpg" }, { "user_id": 2878238, "full_name": "Adair Borges", "title": "Founder", "founder_bio": "Entrepreneurial scientist using evolution to build the next generation of therapeutics for autoimmune disease.\n\nUCSF-trained PhD with expertise spanning parasitology, virology, and genomics. Published over 50 scientific papers, and is a coinventor on a patent for anti-CRISPR proteins. Former Miller Fellow at UC Berkeley, and previously led a venture creation team at an early stage biotech company. Motivated by a lifelong fascination with parasites.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/adair-borges-phd-729857222/", "x_url": "https://x.com/AdairLBorges", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/98a05cbf8d8580bd5412fb6f3b531fce7bedc579.jpg" }, { "user_id": 2929585, "full_name": "Emily Weiss", "title": "Founder", "founder_bio": "I'm a molecular and computational biologist working to revolutionize drug discovery for autoimmune disease. At Ditto, I lead the platform for engineering parasite-derived therapeutics.\n\nI hold a PhD in microbiology, genomics, and plant genetics, with a focus on sequencing, high-throughput screening, and computational pipeline development. Published in Nature Microbiology and eLife. Previously at Illumina, DuPont, and an early-stage biotech venture team.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/emily-weiss-939559124", "x_url": "https://x.com/emilycpierce", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d41f67a5440c5e508182624495024d2db978c018.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 97563, "title": "Ditto Bio – Evolutionary therapies for autoimmune disease", "tagline": "We discover new drugs by harnessing parasite biology", "body": "We're [Adair Borges](https://www.linkedin.com/in/adair-borges-phd-729857222/), [Dennis Sun](https://www.linkedin.com/in/dennis-a-sun/), and [Emily Weiss](https://www.linkedin.com/in/emily-weiss-phd-939559124/), the founders of [Ditto Bio](https://www.ycombinator.com/companies/ditto-biosciences).\n\n**We believe the world's best immunologists live inside of you.** \n\nTLDR; We transform parasite strategies for hacking the immune system into breakthrough drugs for autoimmune disease.\n\nViruses, ticks, and worms are the natural enemies of the human immune system. Over millions of years of experimentation, they've developed extraordinarily effective molecules to prevent your immune system from attacking them. This makes their proteins ideal drug candidates for autoimmune diseases, where a hyperactive immune system attacks the body's own tissues. We're mining the genomes of these organisms to translate these naturally occurring drugs into novel therapies.\\\n\\\n\n\n**The Problem**\n\n90% of drugs fail in clinical trials, when they are tested in humans for the first time. These failures come after companies invest 3-5 years and $15-20M “derisking” drugs in cells and mice. Everyone knows that cells and mice aren’t people, but without a smarter strategy, billions of dollars will continue to be wasted making drugs that will never reach patients.\n\n**Our Approach**\n\nWe start with molecules that _already work_ in humans. Parasites have been running experiments on people for millions of years, creating powerful molecules to control the human immune system. Our AI platform identifies drug-like proteins from these organisms at scale, which we then engineer into safe, effective therapeutics.\n\n**The Best Drugs Come from Nature**\n\nAspirin, penicillin, Botox, and GLP-1 agonists like Ozempic and Wegovy all originated as molecules from other organisms: aspirin from willow bark, penicillin from mold, and GLP-1 agonists from Gila monster venom. We're establishing parasites as the next transformative source of therapeutics.\n\n**Our Team**\n\nWe’re three PhD scientists trained at top institutions (Harvard, Berkeley, UCSF, UCSD) with 40+ years of experience in computational biology, host-parasite interactions, and evolution. We worked together for 3 years as early scientists at another startup before founding Ditto.\n\n![uploaded image](/media/?type=post&id=97563&key=user_uploads/2915094/304ef78d-d58a-499d-9ada-63096be7fb40)\n\n**Our Data Moat**\n\nSince founding seven months ago, we've analyzed 1M+ proteins across primate-infecting viruses, ticks, and parasitic worms. We've discovered thousands of proteins predicted to hit clinically validated human targets. Early experiments show that we have surfaced an untapped source of new drugs that act on high-value targets for immune diseases. Our proteins can achieve extremely tight binding (1–2 nM), among other drug-like properties.\n\nWe’re also building a tissue biobank that lets us map real-world immune memory to parasite proteins. This approach gives us a major advantage in tackling one of the biggest barriers facing all novel protein therapeutics: predicting and designing around immunogenicity.\n\nThe opportunity is vast: >98% of viral and parasite proteins remain uncharacterized. We're systematically mining this unexplored biology at scale to find and deploy nature’s best drugs.\n\n**Our Ask**\n\nIf you’re an industry immunologist, biologics drug developer, or protein designer, please reach out at [founders@dittobio.com](mailto:founders@dittobio.com) to learn where our novel targets, natural scaffolds, and immunogenicity assays could make a difference for your drug programs.", "url": "https://www.ycombinator.com/launches/PNb-ditto-bio-evolutionary-therapies-for-autoimmune-disease", "votes": 161, "created_at": "2026-02-06T01:06:05.580Z" } ] }, { "id": 31484, "name": "AbInitio Bio", "slug": "abinitio-bio", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a7b4f70c5d14e24fb5773ec42e56d354595bd134.png", "website": "https://www.abinitio-bio.com", "all_locations": "Boston, MA, USA", "long_description": "Abinitio Bio builds foundation models for biomanufacturing, validated against wet lab data. Our first model, Echo, predicts manufacturing outcomes more accurately than standard models, and any pharma or CDMO can adapt it to their own workflow instantly. By making process decisions that take 6-18 months today in hours, we help teams avoid the cost of delay on a single program. ", "one_liner": "The Intelligence layer for Drug Manufacturing", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1778703313, "tags": [], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 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/abinitio-bio", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/abinitio-bio.json", "year_founded": 2026, "city": "Boston", "country": "US", "linkedin_url": "https://www.linkedin.com/company/abinitio-bio/?viewAsMember=true", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a7b4f70c5d14e24fb5773ec42e56d354595bd134.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": [ { "user_id": 3323269, "full_name": "Daniel Mukasa", "title": "Founder/CEO", "founder_bio": "Founder & CEO at AbInitio Bio (YC P26). Postdoctoral Fellow at MIT focused on AI and drug discovery. Previously AI/ML intern at Merck working on antibody therapeutic design. PhD in Applied Physics and Materials Science from Caltech.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/daniel-mukasa-2a8927120/", "x_url": "https://x.com/DanielMukasa1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/97cb9523df465c7fd42886512eaf77dd6c280f80.jpg" }, { "user_id": 3776060, "full_name": "Nisha Gopal", "title": "Founder", "founder_bio": "Founder CSO at Abinitio Bio (YC P26). Biochemist with 14 years of experience, Stanford PhD, former research scientist at the The Broad Institute of Harvard and MIT", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/gopalnisha", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d0efc53b1dc5276797580b4e04d7fdbc1c6022a5.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 101683, "title": "AbInitio Bio - Frontier foundation models for biomanufacturing", "tagline": "Foundation models for Biomanufacturing ", "body": "**TL;DR:** Biomanufacturing is bottlenecked by experiments that take years and don't transfer across products. We're building foundation models for biological manufacturing, validated against wet lab data, so decisions that take 6-18 months today can be made in hours, saving $100M+ per program.\n\n![uploaded image](/media/?type=post&id=101683&key=user_uploads/3323269/c03e8ad1-e7f1-4b0c-8417-b22137f847af)\n\n**The problem**\n\nBiologics development is one of the slowest, lowest-yield engineering disciplines in the modern economy. A single CHO cell line campaign costs $5-10M and 12-18 months. Multispecific antibody programs routinely fail at CMC. Every month of delay on a blockbuster costs pharma $100M+ in lost revenue.\n\nEvery product team rebuilds the same process knowledge from scratch because:\n\n* nothing transfers across products, scales, or sites\n* the data that exists sits trapped in PDFs, batch records, and proprietary silos\n* existing tools optimize one product at a time and need expert tuning\n* AI-for-bio has focused on drug discovery and overlooked the value in manufacturing\n\nPharma spends over $100B annually on biologics development and manufacturing, and ships processes with yields and timelines that haven't fundamentally shifted in a decade.\n\n**What we built**\n\n**The first foundation models for biomanufacturing**, designed to close the data gap incumbents can't.\n\nOur first model, Echo, predicts manufacturing outcomes more accurately than standard hybrid models, and any pharma or CDMO can adapt it to their workflow instantly. We're extending the same architecture to cell line engineering, multispecific developability and CMC risk, and upstream aggregation (HMW%) prediction.\n\nThe complex biologics wave is the immediate wedge: 600+ molecules in active clinical trials, \\~15 approved, and a CMC-heavy profile that punishes one-product-at-a-time tooling. Foundation models that transfer across products are the only way to keep up.\n\n**Our story**\n\nI'm Daniel Mukasa, founder and CEO. I’ve spent much of my career at Caltech, Merck, MIT, and the Broad doing drug discovery, just to watch our carefully optimized drugs get killed by manufacturing problems. I’m building the tools we need to fix that across the full biomanufacturing stack.\n\n**Our ask**\n\n**Pharma, biotech, and CDMO intros**. Specifically, cell line development, process development, and CMC leads working on biologics, multispecific, cell and gene therapies, and AAV delivery systems. Also open to BD conversations with platform companies sitting on proprietary process data. \n\nreach us at founders@abinitio-bio.com", "url": "https://www.ycombinator.com/launches/QS3-abinitio-bio-frontier-foundation-models-for-biomanufacturing", "votes": 19, "created_at": "2026-05-22T18:47:27.191Z" } ] }, { "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": 32587, "name": "TareBio", "slug": "tarebio", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9e770bbea41518001119968d389d0d3e35ce7abe.png", "website": "https://tarebio.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Our platform develops personalized or population-level vaccines for any disease in weeks by changing only one input: the immune imprint. Instead of trying to predict which antigens might work, we read out immune cells already fighting the disease in the body and reverse-engineer antigens that activate them. We've already run our platform end-to-end and created a vaccine that cured cancer in mice.", "one_liner": "Vaccines for any cancer or infection in weeks.", "team_size": 1, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1785462754, "tags": [ "Vaccines", "Synthetic Biology", "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/tarebio", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/tarebio.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/tarebio/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9e770bbea41518001119968d389d0d3e35ce7abe.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/96525419f2b8f6b4da09d8c897eb1c8553bef26f.png" ], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 431020, "full_name": "Eta Atolia", "title": "Founder/CEO", "founder_bio": "Founder & CEO, TareBio (YC S26). MD-PhD from UCLA-Caltech, where she built a novel platform that develops vaccines for any cancer or infection in weeks, funded by the Gates Foundation. BS in Biology from MIT. Conducted research at the Koch Institute, Harvard Medical School, and the Weizmann.\n\nSelect Awards: UCLA MD-PhD Merit Scholar, UCLA TDG Senior Fellow, MIT iGEM Gold Medal, MIT IDEAs winner, MIT Dean Advisory Group, WhiteHouse Science Fair, Bessie Lawrence Scholar, Intel STS Finalist", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/eta-atolia/", "x_url": "https://x.com/eatolia", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1b89afa4d7ba0012222ebc57912ad8e652db40bd.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 108804, "title": "TareBio: Vaccines for any cancer or infection in weeks", "tagline": "We make personalized or population-level vaccines reverse-engineered from the immune system. We have already cured cancer in mice and are vaccinating our first patient within the year.", "body": "**TL;DR:** Our platform develops **personalized** or **population-level** vaccines for **_any_ disease** in weeks by **changing _only one input_**: the immune imprint. Instead of trying to predict which antigens might work, we read out immune cells already fighting the disease in the body and reverse-engineer antigens that activate them. We've already run our platform end-to-end and created a vaccine that cured cancer in mice. We will be vaccinating our first patient within a year.\n\n![uploaded image](/media/?type=post&id=108804&key=user_uploads/431020/46cdbd4d-72d9-4231-a781-01a886dd82a4)\n\nView our launch video here: \n\n**The vaccine bottleneck**\n\nFor 200+ years, vaccines have been designed using the same workflow: study a pathogen or tumor, _predict_ which antigens might trigger protection, vaccinate, and hopefully generate a good immune response. This works for simpler infections. It has struggled for decades on complex diseases like HIV, TB, Hep C, and nearly _every cancer_. Currently, vaccine design is treated as a prediction problem.\n\n**What we do differently**\n\nInstead, we treat it as a readout problem. The **immune system has _already solved_** antigen selection in the body, it knows what it's responding to and the best immune cells for the job. So, we flip the whole process around:\n\n1. Capture the immune cells already responding to a cancer or infection, the \"**immune imprint**\"\n2. Use this imprint to directly identify, or “**reverse engineer**”, activating antigens\n3. Build a **vaccine** from those antigens\n\n![uploaded image](/media/?type=post&id=108804&key=user_uploads/431020/31744f0e-4a44-4aa9-aa2e-29d174a8d30a)\n\nThe same platform works for **_any_ disease** by changing one input: the immune imprint. Function over prediction, no constrained search space, and the output is a vaccine that can generate durable immunity in the body, not a transient antibody conjugate or cellular therapy.\n\n**Proof**\n\nWe built the full platform in mice and ran it end-to-end: identified antigens from the immune imprint of the cancer, designed a vaccine from them, and **cured lymphoma in mice.** Nothing about the technology is specific to lymphoma, it generalizes to any cancer or infection. We are using our technology to create both personalized (n=1) and population-level vaccines.\n\n**Why us**\n\nI'm Eta Atolia, founder and CEO. I built this platform over the last 8 years as part of my MD-PhD at UCLA-Caltech with funding from The Gates Foundation. Before this, I did my undergrad at MIT with most of my time dedicated to cancer immunotherapy and infectious disease research at the Koch and HMS. Our scientific advisory board includes Darrell Irvine, PhD (Vice Chair of Immunology at Scripps, Former MIT Professor of 22 years, world-renowned vaccine engineer), Manish Butte, MD, PhD (Chief of Immunology at UCLA and expert rare disease clinician), and John Glaspy, MD, MPH (Endowed Chair and Medical Director of Integrative Oncology at UCLA).\n\n**Our ask**\n\nWe are actively working with organizations and individuals to create both population-level vaccines and personalized treatments.\n\nHelpful things as we build:\n\n* If you have **patient tumor** **or** **blood samples**, please reach out.\n* Intros to **biotech + pharma** and **vaccine teams** for partnerships.\n* **Immunologists, oncologists, infectious disease specialists,** and **ML engineers** who want to build this with us.\n\nIf any of this resonates, reach out at [contact@tarebio.com](mailto:contact@tarebio.com).\n\n![uploaded image](/media/?type=post&id=108804&key=user_uploads/431020/e32e6406-ca9a-4740-9a77-48dd31f02e7f)\n\n", "url": "https://www.ycombinator.com/launches/SIu-tarebio-vaccines-for-any-cancer-or-infection-in-weeks", "votes": 13, "created_at": "2026-08-04T14:27:01.936Z" } ] }, { "id": 33016, "name": "Gamgee", "slug": "gamgee", "former_names": [ "Gamgee Technologies" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6f6bf83f60f82f47f1a3b961153608810edebb3b.png", "website": "https://gamgee.io/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "​Gamgee began with Rosie, founder Paul Conyngham’s dog. After surgery, chemotherapy and immunotherapy failed, Rosie was given months to live. Paul used AI and genomic analysis to identify targets in her tumour, then worked with scientists at UNSW to produce what is believed to be the first fully computationally designed mRNA cancer vaccine delivered to a dog. Following treatment, several tumours shrank significantly and her quality of life improved\r\n\r\nGamgee is building an end to end platform for personalised mRNA cancer vaccines for dogs. We sequence a dog’s tumour and healthy DNA, identify mutations unique to its cancer, and design a vaccine that teaches the immune system what to attack. We manage the entire process- from vet intake and tissue collection through genomic analysis, vaccine production, treatment and response monitoring\r\n\r\n​We are now turning that one off effort into a repeatable clinical system. Gamgee is running clinical trials in Australia, working with vets, oncologists and specialised laboratories, and accepting cases globally. Our goal is to make personalised cancer treatment accessible to every dog that needs it- and use each case to improve the treatment for the next", "one_liner": "Curing cancer. Starting with dogs.", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1784748212, "tags": [ "Biotech", "Genomics", "Oncology", "AI" ], "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", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/gamgee", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/gamgee.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/109672135", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6f6bf83f60f82f47f1a3b961153608810edebb3b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 564809, "full_name": "Paul Conyngham", "title": "Founder/CEO", "founder_bio": "Founder at Gamgee Technologies (YC S26), building personalised mRNA cancer vaccines for dogs.\n\nGamgee grew out of my work using AI and genomics to help my dog Rosie fight cancer. \n\nPreviously co-founded Core Intelligence, building applied AI systems across startups and enterprise, and helped grow Australia’s AI community as a founding member of the Data Science & AI association of Australia. \n\nBackground in ML, data science and electrical and computer systems engineering", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/paustevenlconyngham/", "x_url": "https://x.com/paul_conyngham", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4fda91993052b2e444cfffa9d59e27aa42567028.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 109527, "title": "Gamgee: Personalised mRNA cancer vaccines for dogs", "tagline": "Input the tumour. Output the vaccine. 300+ dogs on the waitlist.", "body": "**We believe that AI meets biology is the biggest opportunity of the 21st century.** We are using AI to build autonomous systems that read biological failure, discover how to control it, and design precise repairs. We are starting with the first computationally personalised mRNA cancer vaccine for dogs.\\\n\\\n**My dog Rosie was dying of cancer.** I designed her an mRNA vaccine. Several of her tumours shrank 50-75%. I am not a biologist - I spent 17 years in machine learning. Doing it by hand took a year and around $20,000. Gamgee is turning that process into software. Our target is a vaccine design in 48 hours and an end-to-end cost of $2,000\n\n\n\n**How it works**\n\n* We **send you a collection kit** to take to your vet or veterinary oncologist. Your vet collects the tumour and matched healthy samples, which are returned for DNA and RNA sequencing.\n* Our software finds and ranks the mutations worth targeting, and keeps the evidence behind every call.\n* **Out comes an mRNA vaccine** design ready for manufacture. Gamgee designs the vaccine and oversees and coordinates every step through to administration by the treating veterinarian.\n\n\\\n**The market**\n\nApproximately **six million dogs** are **diagnosed with cancer** every year in the United States, and cancer is the leading cause of death in adult dogs. Owners who pursue specialist treatment spend thousands on surgery, chemotherapy and radiation.\\\n\\\n\\\n**The compounding data advantage**\n\nEvery dog we treat returns sequencing, immune and outcome data for a species where datasets remain scarce. That data improves target selection, which improves the next vaccine, which produces better data. We are not just early to the problem - we are the ones accumulating the data that solves it.\n\n\\\n**Proof so far**\n\n* Full computational pipeline built and executed end-to-end as a non-biologist using AI\n* 50-75% shrinkage in the responsive tumours. \n* Microscopic analysis found substantial IBA1+ macrophage and CD3+ T-cell infiltration in Rosie’s responsive lesions, with little in the non-responsive tumour.\n* Second-generation vaccine completed and ready\n* Over a billion impressions on Rosie's story.\n* Backed by Y Combinator (S26) \n\nRosie was one patient. Our next step, already well underway, is a clinician-led trial in 30 dogs to evaluate safety, immune response and clinical outcomes.\n\n\\\n**Why dogs first, and not only dogs**\n\nMost modern dog breeds were created within the last few hundred years from small founder populations and then bred within closed gene pools. This concentrates inherited cancer risk, leaving many breeds unusually prone to particular cancers.\\\n\\\nPet dogs develop cancer naturally, not in a laboratory. Their immune systems are intact and their disease progresses on timelines that let us learn quickly. Dogs are a real market in their own right and the fastest place to get good at personalised cancer vaccines.\n\n\\\n**Our ask**\n\n* **If your dog has cancer, please do get in touch about our upcoming clinical trial.**\n* Veterinary oncologists and specialty hospitals who want to be a trial site.\n* Investors in longevity or personalised medicine - would rather meet you before the trial reads out than after.\n\nWe are building Gamgee so that no other dog waits for hope which arrives too late.\n\nReach me at [paul@gamgee.io](mailto:paul@gamgee.io) or [gamgee.io](http://gamgee.io)\\\n\\\nI love you Rosie.", "url": "https://www.ycombinator.com/launches/SUZ-gamgee-personalised-mrna-cancer-vaccines-for-dogs", "votes": 186, "created_at": "2026-08-11T17:00:00.187Z" } ] } ]