This virtual Biotech company put 37,000 AI Agents to work on drug discovery

Developing a new drug can take years and cost hundreds of millions of dollars, and even then, most candidates ultimately fail. Now, researchers at Stanford University have built a virtual biotech company with 37,000 Artificial Intelligence (AI) agents that work together to analyse drug targets and design therapies.

Roughly 90 percent of drugs that enter clinical trials never reach the market. That’s often because promising results in the lab don’t translate to patients, or the drug causes dangerous side-effects not caught earlier in the development process.

Part of the problem is the evidence that could help catch these issues earlier in the process is scattered across disciplines and formats, making it hard for any single team to weigh it all.

To get around this, a Stanford team created a system they call a virtual biotech, which consists of up to 37,000 AI agents built to mimic the divisions of a real drug-development company. In a paper published in Science, the system identified which types of drug targets are more likely to succeed in clinical trials and even proposed a lung cancer treatment that a major drugmaker later landed on too.

“Our idea was to see how far we could push this. Could we create a biotech company that takes on everything from looking for drug targets all the way to designing clinical trials?” senior author James Zou said in a press release.

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The new system features a virtual Chief Scientific Officer (CSO) that takes a query from a human user and then delegates tasks to an army of specialised “Robo-Scientist” agents working on the problem.

These agents are armed with their own databases and tools and are split into one of four divisions that specialise in finding and validating drug targets, assessing safety risks, choosing how a drug should be delivered, and reviewing existing clinical trial data. The system has built-in access to the Open Targets database, a massive public repository of clinical trial data.

To test the system, the researchers gave it an existing study showing that genetic evidence can help predict which drugs succeed in trials and asked it how to build on that research. The CSO decided the first step was to improve the quality of the data it had access to because many trials in the Open Targets database don’t clearly record whether the drug actually worked.

So, it asked its researcher agents to dig through the outcomes of 37,075 individual Phase II and III clinical trials, assigning one agent to each trial. The agents searched trial registries, published papers, and press releases for results. They crunched through the job in about six hours - a fraction of the time it would take a team of humans.

The CSO asked another agent to look for promising gene candidates by scouring a public database of human tissues showing which genes are switched on in which cell types. It came up with a two-part scoring system, which first measured whether a gene was active in just one type of cell or across many and then gauged whether its activity was controlled more like an on-off switch or could be dialed up and down like a dimmer switch.

Comparing those scores to the updated trial outcome data revealed a pattern. Drugs aimed at switch-like genes only found in a small number of cell types were 48 percent more likely to eventually reach the market, 40 percent more likely to advance from Phase 1 to Phase 2 trials, and had 32 percent fewer adverse events than drugs hitting more broadly active targets.

The researchers then pushed the system further, asking it to evaluate a protein called B7-H3 that’s associated with lung cancer. The agents discovered the protein was particularly common in connective-tissue cells called fibroblasts that are often found close to tumor cells.

The agents then discovered evidence those cells were suppressing the activity of nearby immune cells, preventing the body from detecting and reacting to the tumors. The system proposed a therapy that would tag cells expressing B7-H3 with an antibody to help direct a toxic chemotherapy drug to them.

The virtual biotech came up with its solution based solely on data available before January 2025, but in August of that year a major pharmaceutical company arrived at the same strategy independently, when its B7-H3-targeted therapy ifinatamab deruxtecan received FDA breakthrough therapy status.

“This was really exciting as an independent, third-party validation that’s consistent with the effects and the design proposed by the virtual biotech,” Zou said.

However, coming up with drug targets is just one step in a long, expensive drug discovery process. While refining the candidate selection process could prevent drug companies from pursuing some obvious dead ends, it can’t speed up the rigorous lab testing and clinical trials required to get a drug to market.

Nonetheless, given the industry’s woeful record at translating promising science into finished products, an army of AI scientists that can significantly speed up a critical part of the drug discovery pipeline could be just what the doctor ordered.

First published on Fanatical Futurist on 17 September 2026.

311 Intelligence

What this means

Analysis by the 311 Strategic Intelligence Unit

The big shift is from one AI tool helping scientists to a whole company of AI agents doing the work. Stanford's virtual biotech runs up to 37,000 agents under a virtual Chief Scientific Officer, and it reviewed 37,075 clinical trials in about six hours. We rate Agentic Artificial Intelligence, AI that decides and acts on its own to reach a goal, a Prepare technology and expect wide use by 2028. We also track AI Agent Process Redesign, firms rebuilding whole processes so AI agents run them with people overseeing, as a Prepare trend.

What it means by sector.

• Pharmaceuticals and biotech: roughly 90 percent of drugs that enter clinical trials never reach the market. Picking targets that are 48 percent more likely to succeed attacks the most expensive failure in the industry.
• Health systems and regulators: the agents' B7-H3 idea matched a therapy that later won FDA breakthrough therapy status. Agent-built evidence will increasingly sit alongside human research.
• Research organisations: the bottleneck moves from analysis to lab testing and clinical trials, which the article notes AI cannot yet speed up.

What to do now.

• Map where your research evidence is scattered across teams and formats, because that is where agents add most value.
• Clean up outcome data first: the virtual CSO's own first step was to fix trial records that did not say whether a drug worked.
• Keep human scientists in charge of what gets tested, and use agents to rule out dead ends early.

The Starburst technologies behind this story

From the 311 Technology Starburst (first product, expected wide use and readiness):

Technology	Category	First product	Wide use	Action	TRL
Agentic Artificial Intelligence	Intelligence	2024	2028	Prepare	8
Antibody Drug Conjugates	Biotech	2000	2030	Prepare	9

And the trends they feed, from the 311 Trends Starburst:

Trend	Wide use	Action
Late Stage AI Drugs	2031	Explore
AI Agent Process Redesign	2030	Prepare

How this moves the trend

AI has been moving deeper into drug research for years. In 2022 we reported that AI was getting great at simulating and designing proteins and drugs, and in 2024 scientists built the first autonomous AI scientist. In March 2026 Roche moved to speed up drug development and doubled down on Nvidia. Stanford's virtual biotech goes further, running a whole research company of agents.

Go deeper with 311

• The 311 Starburst Centre: every technology and trend above, with timelines and readiness.
• 311 Vantage: ask what AI agents in drug discovery mean for your organisation and get a plain English executive brief.
• The Exponential Technology Codex and the Annual Trends Codex: the technologies and trends reshaping every industry.
• Book a briefing or keynote on the future of AI, life sciences and healthcare for your board or leadership team.