The Virtual Biotech: A multi-agent AI framework for therapeutic discovery and development.

Abstract

Drug development requires evidence integration across biological scales and modalities, but relevant tools are fragmented. We introduce the Virtual Biotech, an organization of artificial intelligence (AI) agents modeled on a drug-development company, with agentic divisions spanning target discovery, safety assessment, modality selection, and clinical development. We demonstrate its utility at three drug-development decision points. First, 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. Second, it integrated multimodal evidence to propose a therapeutic strategy in lung cancer. Third, it analyzed a terminated ulcerative colitis trial and inferred potential mechanisms of failure. These results demonstrate that human-guided multi-agent systems can conduct transparent, multiscale analyses to inform therapeutic-development decisions.