Biomedical Text Mining and Ontologies
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Biomni envisions artificial intelligence augmenting human scientists and accelerating discovery by interpreting multi-modal datasets, optimizing protein stability, orchestrating wet-lab instruments, and generating experimentally testable protocols.
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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.
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@article{Huang2026Autonomous,
title = {Autonomous biomedical research with an artificial intelligence agent},
author = {Kexin Huang and Serena Zhang and Hanchen Wang and Yuanhao Qu and Yingzhou Lu and Ryan Li and Yusuf Roohani and Lin Qiu and Shiyi Cao and G. L. Li and Junze Zhang and Di Yin and Rick P. Wierenga and Deniz Kavi and Liu S and Tianwei She and Shruti Marwaha and Jennefer N. Carter and Xin Yi Zhou and Matthew T. Wheeler and Jonathan A. Bernstein and Mengdi Wang and Peng He and Jingtian Zhou and Michael Snyder and Le Cong and Aviv Regev and Jure Leskovec},
journal = {Science},
year = {2026},
doi = {10.1126/science.adz4351},
url = {https://doi.org/10.1126/science.adz4351}
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