Autonomous biomedical research with an artificial intelligence agent

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Authors: Kexin Huang, Xicheng Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Ryan Li, Yusuf Roohani, Lin Qiu, Shiyi Cao, G. L. Li, J Zhang, Di Yin
+16 more Rick Wierenga, Deniz Kavi, Liu S, Tianwei She, Shruti Marwaha, J T Carter, Xin Zhou, Matthew T. Wheeler, Jonathan A. Bernstein, Mengdi Wang, Peng He, Jingtian Zhou, M Snyder, Le Cong, Aviv Regev, Jure Leskovec
Cited by: 11
References: 164

Abstract

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 multimodal datasets, optimizing protein stability, orchestrating wet-lab instruments, and generating experimentally testable protocols. Biomni envisions artificial intelligence augmenting human scientists and accelerating discovery.

Fields of Study & Subjects

Biomedical Text Mining and Ontologies Genomics and Rare Diseases Topic Modeling

Publication details

Journal / source
Science
Publisher
American Association for the Advancement of Science (AAAS)
Volume
393
Issue
6813
Pages
eadz4351–eadz4351
Publication date
9 July 2026
Language
EN