{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T08:53:44Z","timestamp":1792918424947,"version":"4.5.0"},"posted":{"date-parts":[[2026,2,23]]},"group-title":"Genomics","reference-count":95,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,2,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Drug discovery and development requires integrating diverse evidence across biological scales and data modalities. However, relevant data, tools, and expertise remain fragmented across teams and organizations, making integration difficult. To address these challenges, we introduce the Virtual Biotech, a coordinated team of AI agents that mirrors the structure of human therapeutic research organizations to support end-to-end computational discovery. The Virtual Biotech is led by a Chief Scientific Officer agent that receives scientific queries, delegates them to domain-specialized scientist agents, and integrates their outputs through data-driven reasoning. Scientist agents leverage complementary tools and knowledge sources spanning statistical genetics, functional genomics, pathways and interactions, chemoinformatics, disease biology, and clinical data. We showcase the Virtual Biotech across three translational applications. First, the agents autonomously annotated and analyzed outcomes from 55,984 clinical trials to identify genomic features of drug targets associated with trial success. More than 37,000 clinical-trialist agents curated structured trial outcomes and linked targets to multi-omic annotations, including cell-type-specific features derived by the agents from single-cell RNA-sequencing atlases. The agents discovered that drugs targeting cell-type-specific genes were 40% more likely to progress from Phase I to Phase II and 48% more likely to reach market (Phase IV), while exhibiting 32% lower adverse event rates. Second, the Virtual Biotech evaluated B7-H3 as a lung cancer target, integrating statistical genetics, single-cell, spatial, and clinicogenomic evidence to propose an antibody\u2013drug conjugate strategy while identifying key liabilities and differentiation opportunities. Third, the platform analyzed a terminated ulcerative colitis trial targeting OSMR\n                  <jats:italic>\u03b2<\/jats:italic>\n                  to infer potential failure mechanisms and proposed biomarker-guided enrollment strategies to address precision-medicine gaps. Together, these results illustrate how the Virtual Biotech can enable more transparent, efficient, and comprehensive multi-scale therapeutic analyses, helping to accelerate early-stage drug discovery workflows while keeping human scientists in the loop.\n                <\/jats:p>","DOI":"10.64898\/2026.02.23.707551","type":"posted-content","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:40:13Z","timestamp":1771922413000},"source":"Crossref","is-referenced-by-count":6,"title":["The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and Development"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6679-1464","authenticated-orcid":false,"given":"Harrison G.","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Eckmann","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Miao","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University"},{"name":"Department of Genetics, Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew B.","family":"Mahon","sequence":"additional","affiliation":[{"name":"PHD Biosciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University"},{"name":"Department of Computer Science, Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026022511350662000_2026.02.23.707551v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhealeco.2016.01.012"},{"key":"2026022511350662000_2026.02.23.707551v1.2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.1166"},{"issue":"7","key":"2026022511350662000_2026.02.23.707551v1.3","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1016\/j.apsb.2022.02.002","article-title":"Why 90% of clinical drug development fails and how to improve it?","volume":"12","year":"2022","journal-title":"Acta Pharmaceutica Sinica B"},{"key":"2026022511350662000_2026.02.23.707551v1.4","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.2786"},{"issue":"1","key":"2026022511350662000_2026.02.23.707551v1.5","doi-asserted-by":"crossref","first-page":"9537","DOI":"10.1038\/s41467-025-64552-2","article-title":"Dynamic clinical trial success rates for drugs in the 21st century","volume":"16","year":"2025","journal-title":"Nature Communications"},{"key":"2026022511350662000_2026.02.23.707551v1.6","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxx069"},{"key":"2026022511350662000_2026.02.23.707551v1.7","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.conctc.2018.08.001","article-title":"Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review","volume":"11","year":"2018","journal-title":"Contemporary clinical trials communications"},{"issue":"1","key":"2026022511350662000_2026.02.23.707551v1.8","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1146\/annurev-biodatasci-102523-103838","article-title":"Human genetics and genomics for drug target identification and prioritization: Open targets\u2019 perspective","volume":"7","year":"2024","journal-title":"Annual review of biomedical data science"},{"key":"2026022511350662000_2026.02.23.707551v1.9","doi-asserted-by":"publisher","DOI":"10.1038\/nrd1608"},{"issue":"1","key":"2026022511350662000_2026.02.23.707551v1.10","first-page":"161","article-title":"Andrew E Mulberg","volume":"16","year":"2021","journal-title":"Paul Howard, and Eric D Marsh. 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