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Agentic AI and the rise of in silico team science in biomedical research | Nature Biotechnology
URL: https://www.nature.com/articles/s41587-026-03035-1
Saved: 2026-03-18T19:50:08.827Z
Perspective Published: 24 February 2026 Agentic AI and the rise of in silico team science in biomedical research Binglan Li, Anil Kumar Saini, Jose Guadalupe Hernandez & Jason H. Moore
Nature Biotechnology (2026)Cite this article
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Abstract
Agentic artificial intelligence (AI) systems are emerging as teams of intelligent computational experts capable of rivaling human performance in labor-intensive tasks, including literature review, hypothesis formulation, data analysis and model interpretation. These systems are poised to accelerate labor-intensive biomedical research by making autonomous decisions based on contextual information and expert feedback. Agentic AI systems have been developed for various applications, including drug discovery, data analysis and biomarker identification; however, several distinct challenges remain for making these systems broadly deployable in biomedical research. Here we discuss three key algorithms and seven foundational building-block characteristics that contribute to the development of agentic AI systems. We highlight their biomedical applications, design considerations and the challenges and opportunities associated with deploying agentic AI systems to advance collaborative scientific research.
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Additional access options: Log in Learn about institutional subscriptions Read our FAQs Contact customer support Similar content being viewed by others What are the limits to biomedical research acceleration through general-purpose AI? Article Open access 12 January 2026 The role of agentic artificial intelligence in healthcare: a scoping review Article Open access 14 March 2026 Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research Article 26 November 2024 References
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Acknowledgements
The authors thank members of the Department of Computational Biomedicine at Cedars-Sinai Medical Center for their valuable comments on this study. The work was supported by the National Institutes of Health (grants R01 LM010098, R01 LM014572, P30 AG094848 and U01 AG066833).
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These authors contributed equally: Binglan Li, Anil Kumar Saini, Jose Guadalupe Hernandez.
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Cedars-Sinai Medical Center, Los Angeles, CA, USA
Binglan Li, Anil Kumar Saini, Jose Guadalupe Hernandez & Jason H. Moore
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J.H.M. conceived the idea for this study and supervised the project. B.L. wrote the first draft of the manuscript. All authors contributed to the writing, revised the manuscript and approved the final version of the manuscript.
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Correspondence to Jason H. Moore.
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Li, B., Saini, A.K., Hernandez, J.G. et al. Agentic AI and the rise of in silico team science in biomedical research. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03035-1
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Received 21 July 2025
Accepted 29 January 2026
Published 24 February 2026
Version of record 24 February 2026
DOI https://doi.org/10.1038/s41587-026-03035-1
Subjects Computational biology and bioinformatics Drug discovery