Using thousands of AI agents to uncover new leads in biological data

What if scientists could uncover new clues about Alzheimer’s and Parkinson’s in minutes instead of months? Working together, OpenAI, PerturbAI ⁠ (opens in a new window) , and Scripps Research ⁠ (opens in a new window) used thousands of AI agents powered by GPT‑Rosalind ⁠ (opens in a new window) to analyze and interpret existing experimental data from millions of mouse brain cells ⁠ (opens in a new window) in just 32 minutes. The agents connected the effects of gene disruptions with findings from scientific literature, uncovering unexpected biological connections that can help scientists understand disease and identify research directions for new treatments. The work shows how AI can help researchers explore more of what large biological experiments reveal.

Modern experiments generate far more data than scientists can thoroughly investigate. Researchers can now alter thousands of genes and measure their effects across millions of cells. But understanding what these results mean for human disease requires connecting complex experimental findings with decades of scattered scientific knowledge. This is where AI can help scientists uncover promising leads that might otherwise be missed.

—Sarah Teichmann, Ph.D., Chair in Stem Cell Medicine at the University of Cambridge and founding co-chair of the Human Cell Atlas

The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas ⁠ (opens in a new window) , a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms.

The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms.

Some of these insights point to new leads for understanding brain disease. The agents identified unexpected differences in how two disease-linked genes affect brain cells differently, as well as a potential connection between cholesterol regulation and neurodegeneration. These findings could help scientists investigate how disease develops and where future treatments might intervene.