Huang Kexin, Zhang Serena, Wang Hanchen, Qu Yuanhao, Lu Yingzhou, Roohani Yusuf, Li Ryan, Qiu Lin, Li Gavin, Zhang Junze, Yin Di, Marwaha Shruti, Carter Jennefer N, Zhou Xin, Wheeler Matthew, Bernstein Jonathan A, Wang Mengdi, He Peng, Zhou Jingtian, Snyder Michael, Cong Le, Regev Aviv, Leskovec Jure
Department of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Research and Early Development, Genentech, South San Francisco, CA, USA.
bioRxiv. 2025 Jun 2:2025.05.30.656746. doi: 10.1101/2025.05.30.656746.
Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse biomedical subfields. To systematically map the biomedical action space, Biomni first employs an action discovery agent to create the first unified agentic environment - mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains. Built on this foundation, Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling it to dynamically compose and carry out complex biomedical workflows - entirely without relying on predefined templates or rigid task flows. Systematic benchmarking demonstrates that Biomni achieves strong generalization across heterogeneous biomedical tasks - including causal gene prioritization, drug repurposing, rare disease diagnosis, microbiome analysis, and molecular cloning - without any task-specific prompt tuning. Real-world case studies further showcase Biomni's ability to interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols. Biomni envisions a future where virtual AI biologists operate alongside and augment human scientists to dramatically enhance research productivity, clinical insight, and healthcare. Biomni is ready to use at https://biomni.stanford.edu, and we invite scientists to explore its capabilities, stress-test its limits, and co-create the next era of biomedical discoveries.
生物医学研究是我们理解人类健康与疾病、药物发现及临床护理取得进展的基础。然而,随着复杂实验室实验、大型数据集、众多分析工具以及海量文献的不断增加,生物医学研究越来越受到重复且碎片化工作流程的制约,这些流程减缓了发现速度并限制了创新,凸显了对一种全新方式来扩展科学专业知识的需求。在此,我们介绍Biomni,这是一种通用的生物医学人工智能代理,旨在自主执行跨不同生物医学子领域的广泛研究任务。为了系统地绘制生物医学行动空间,Biomni首先使用一个行动发现代理来创建首个统一的代理环境——从25个生物医学领域的数万篇出版物中挖掘关键工具、数据库和协议。在此基础上,Biomni具有一种通才代理架构,该架构将大语言模型(LLM)推理与检索增强规划及基于代码的执行相结合,使其能够动态组合并执行复杂的生物医学工作流程——完全无需依赖预定义模板或严格的任务流程。系统基准测试表明,Biomni在异构生物医学任务中实现了强大的泛化能力——包括因果基因优先级排序、药物再利用、罕见病诊断、微生物组分析和分子克隆——无需任何特定任务的提示调整。实际案例研究进一步展示了Biomni解释复杂多模态生物医学数据集并自主生成可实验测试协议的能力。Biomni设想了一个未来,虚拟人工智能生物学家与人类科学家并肩工作并增强其能力,以大幅提高研究生产力、临床洞察力和医疗保健水平。可在https://biomni.stanford.edu上使用Biomni,我们邀请科学家探索其功能,对其极限进行压力测试,并共同创造生物医学发现的新时代。