Stanford researchers will discuss their agentic ‘scientists’ that are on course to reshape drug discovery at VB Transform 2026

Drug discovery is notoriously inefficient. Pharmaceutical projects span years, moving from one specialized human team to the next through disconnected workflows that result in knowledge loss during each handoff. A shocking 90% to 95% of drug discovery projects reportedly fail —… Drug discovery is notoriously inefficient. Pharmaceutical projects span years, moving from one specialized human team to the next through disconnected workflows that result in knowledge loss during each handoff. A shocking 90% to 95% of drug discovery projects reportedly fail — one of the highest failure rates of any industry. A single successful drug can take over a dozen years and up to $1 billion from initial discovery to patient distribution, according to published reports. Generative AI is being used to solve some of the challenges, but Stanford researchers have moved the ball forward with agentic AI. A team led by James Zou, associate professor of Biomedical Data Science at Stanford University, has deployed thousands autonomous AI "scientist" agents in a virtual biotech that simulates the full lifecycle of drug development. The agents handle everything from initial discovery through safety testing and clinical trial design, while maintaining the continuity that’s lacking in today’s drug discovery processes, according to Zou.The project uses a hierarchical…