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Generalist biological artificial intelligence in modeling the language of life

Nature Biotechnology (2026)Cite this article

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Abstract

Generalist biological artificial intelligence (GBAI) represents a transformative approach to modeling the ‘language of life’—the flow of information from DNA to cellular function. This Review synthesizes rapid advances in biological AI to interpret and generate DNA, RNA, proteins and cellular systems. We chart a course toward comprehensive systems that can concurrently process and predict across these domains, performing several critical biological tasks simultaneously. Substantial opportunities lie in synergizing language and structural AI, leveraging specialized models and improving AI agents for autonomous discovery. After addressing challenges in data, biological complexity, scalability and experimental validation, GBAI has the potential to deepen our understanding of disease pathways and biomarkers, advance automated therapeutic design and evaluation, and integrate within virtual cells to meaningfully simulate biological activity.

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Fig. 1: Vision for GBAI.
Fig. 2: Applications of biological AI across different dimensions of cellular processing.
Fig. 3: Overview of the challenges faced by current biological AI algorithms.

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Authors and Affiliations

  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Vishwanatha M. Rao, Serena Zhang, Marinka Zitnik & Pranav Rajpurkar

  2. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Vishwanatha M. Rao

  3. Department of Computer Science, Stanford University, Stanford, CA, USA

    Serena Zhang

  4. Arc Institute, Palo Alto, CA, USA

    Brian S. Plosky & Patrick D. Hsu

  5. Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA

    Patrick D. Hsu

  6. Vector Institute, Toronto, Ontario, Canada

    Bo Wang

  7. University Health Network, Toronto, Ontario, Canada

    Bo Wang

  8. University of Toronto, Toronto, Ontario, Canada

    Bo Wang

  9. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    James Zou

  10. Scripps Research, La Jolla, CA, USA

    Eric J. Topol

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  1. Vishwanatha M. Rao
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Contributions

V.M.R., E.J.T. and P.R. conceptualized the study. V.M.R. performed the investigation and contributed to writing the original draft. V.M.R., S.Z., B.S.P., P.D.H., B.W., J.Z., M.Z., E.J.T. and P.R. contributed to writing, reviewing and editing. S.Z. and V.M.R. were responsible for visualization. E.J.T. and P.R. were responsible for supervision.

Corresponding authors

Correspondence to Eric J. Topol or Pranav Rajpurkar.

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Competing interests

P.D.H. acknowledges outside interest as a cofounder of Stylus Medicine, Terrain Biosciences and Monet AI; serves on the board of directors at Stylus Medicine; is a board observer at EvolutionaryScale and Terrain Biosciences; is a scientific advisory board member at Arbor Biosciences and Veda Bio; and is an advisor to NFDG, Varda Space and Vial Health. J.Z. is an advisor for Amgen, Together AI, InVision and Fidocure. E.J.T. reports receiving personal fees from Abridge, Danaher, Mercor and Flagship Pioneering. The remaining authors declare no competing interests.

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Rao, V.M., Zhang, S., Plosky, B.S. et al. Generalist biological artificial intelligence in modeling the language of life. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03064-w

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