Autonomous biomedical research with an artificial intelligence agent

Автономное биомедицинское исследование с использованием агента искусственного интеллекта
Aviv Regev, Michael P. Snyder, Jure Leskovec, Matthew Thomas Wheeler, Le Cong, Tianwei She, Peng He, Kexin Huang, Jonathan Adam Bernstein, Shruti Marwaha, Jonathan Adam Bernstein, Hanchen Wang, Jingtian Zhou, Xin Zhou, Mengdi Wang, Yusuf Roohani, Yuanhao Qu, Di Yin, Serena Zhang, G. L. Li, Jennefer N. Carter, Liu S, Yingzhou Lu, Ryan Li, Lin Qiu, Shiyi Cao, Junze Zhang, Rick P. Wierenga, Deniz Kavi, J T Carter
2026-07-09

Biomniagentic environmentbiomedical artificial intelligence agentdrug repurposingretrieval-augmented planning
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.
1
An action-discovery agent mines tools, databases, and protocols from thousands of publications to construct a unified biomedical agentic environment.
2
Benchmarking indicates strong generalization across causal gene prioritization, drug repurposing, rare-disease diagnosis, microbiome analysis, and molecular cloning without task-specific tuning.
3
Biomni combines large-language-model reasoning, retrieval-augmented planning, and code execution to dynamically compose workflows without predefined templates.
4
Biomni is a general-purpose biomedical AI agent that autonomously executes diverse research tasks across 25 biomedical domains.
5
Case studies show Biomni can interpret multimodal datasets, optimize protein stability, coordinate wet-lab instruments, and generate experimentally testable protocols.

Biomni, a general-purpose biomedical artificial intelligence agent

autonomous execution and generalization of diverse biomedical research tasks through dynamically composed workflows

Publication Details
Publication Date
2026-07-09
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Authors
Aviv Regev
Michael P. Snyder
Jure Leskovec
Matthew Thomas Wheeler
Le Cong
Tianwei She
Peng He
Kexin Huang
Jonathan Adam Bernstein
Shruti Marwaha
Jonathan Adam Bernstein
Hanchen Wang
Jingtian Zhou
Xin Zhou
Mengdi Wang
Yusuf Roohani
Yuanhao Qu
Di Yin
Serena Zhang
G. L. Li
Jennefer N. Carter
Liu S
Yingzhou Lu
Ryan Li
Lin Qiu
Shiyi Cao
Junze Zhang
Rick P. Wierenga
Deniz Kavi
J T Carter
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