# Virtual Biotech

This work introduces the Virtual Biotech, an organization of AI agents modeled on a drug-development company with agentic divisions spanning target discovery, safety assessment, modality selection, and clinical development, and demonstrates its utility at three drug-development decision points: over 37,000 agents annotated outcomes from 55,984 trials and found that drugs targeting cell-type-specific genes were 48% more likely to reach market with 32% fewer adverse events; it integrated multimodal evidence to propose a therapeutic strategy in lung cancer; and it analyzed a terminated ulcerative colitis trial and inferred potential mechanisms of failure.

This work introduces the Virtual Biotech, an organization of AI agents modeled on a drug-development company with agentic divisions spanning target discovery, safety assessment, modality selection, and clinical development, and demonstrates its utility at three drug-development decision points: over 37,000 agents annotated outcomes from 55,984 trials and found that drugs targeting cell-type-specific genes were 48% more likely to reach market with 32% fewer adverse events; it integrated multimodal evidence to propose a therapeutic strategy in lung cancer; and it analyzed a terminated ulcerative colitis trial and inferred potential mechanisms of failure.

The study developed RepurAgent, a hierarchical multi-agent AI system in which a supervisor agent and a planning agent coordinate four specialized sub-agents (research, prediction, data, and report) through a human-in-the-loop design with episodic memory and retrieval-augmented generation, and validated it across three scenarios spanning the drug repurposing lifecycle: in Acute Myeloid Leukemia a blinded expert evaluation indicated substantially more novel and mechanistically credible candidates than a vanilla LLM baseline; in a retrospective COVID-19 antiviral screen it prioritized compounds with AUC-ROC up to 0.

The study developed RepurAgent, a hierarchical multi-agent AI system in which a supervisor agent and a planning agent coordinate four specialized sub-agents (research, prediction, data, and report) through a human-in-the-loop design with episodic memory and retrieval-augmented generation, and validated it across three scenarios spanning the drug repurposing lifecycle: in Acute Myeloid Leukemia a blinded expert evaluation indicated substantially more novel and mechanistically credible candidates than a vanilla LLM baseline; in a retrospective COVID-19 antiviral screen it prioritized compounds with AUC-ROC up to 0.