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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

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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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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.

Authors and Affiliations

Cedars-Sinai Medical Center, Los Angeles, CA, USA

Binglan Li, Anil Kumar Saini, Jose Guadalupe Hernandez & Jason H. Moore

Contributions

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.

Corresponding author

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

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