Autonomous biomedical research with an artificial intelligence agent
- Kexin Huang
- Serena Zhang
- Hanchen Wang
- Yuanhao Qu
- Yingzhou Lu
- Ryan Li
- Yusuf Roohani
- Lin Qiu
- Shiyi Cao
- Gavin Li
- Junze Zhang
- Di Yin
- Rick Wierenga
- Deniz Kavi
- Sherry Liu
- Tianwei She
- Shruti Marwaha
- Jennefer N. Carter
- Xin Zhou
- Matthew T. Wheeler
- Jonathan A. Bernstein
- Mengdi Wang
- Peng He
- Jingtian Zhou
- Michael P. Snyder
- Le Cong
- Aviv Regev
- Jure Leskovec
2026-07-09
Biomedical research is increasingly constrained by repetitive, fragmented workflows that slow discovery. We introduce Biomni, a general-purpose biomedical artificial intelligence agent that autonomously executes diverse research tasks. To map the biomedical action space, Biomni’s action-discovery agent mines tools, databases, and protocols from thousands of publications across 25 domains, building a unified agentic environment. Its general-purpose architecture integrates large language model reasoning with retrieval-augmented planning and code-based execution, dynamically composing workflows without predefined templates. Systematic benchmarking shows strong generalization across heterogeneous tasks—causal gene prioritization, drug repurposing, rare-disease diagnosis, microbiome analysis, and molecular cloning—without task-specific tuning. Real-world case studies demonstrate Biomni interpreting multi-modal datasets, optimizing protein stability, orchestrating wet-lab instruments, and generating experimentally testable protocols. Biomni envisions artificial intelligence augmenting human scientists and accelerating discovery.