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    Artificial intelligence in drug discovery — what it is, where we stand and the path forward

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    Artificial intelligence in drug discovery — what it is, where we stand and the path forward

    Abstract

    Artificial intelligence (AI) in drug discovery has attracted increasing interest over the past decade. It is now time for a critical review of progress in the field: where did we advance — and where are we yet to see impact — when it comes to what matters in drug discovery, which is to deliver safer and more efficacious medicines to patients faster? Although a wide variety of AI methods have been developed, applied and benchmarked, evidence of their clinically relevant impact is, so far, disappointingly limited. In this Perspective we discuss potential reasons, including an insufficient focus on clinical translation during model development, difficulties with applying AI algorithms on conditional life science data, and insufficient problem definitions and the resulting underspecification of computational models for real-world use cases. ‘Technology push’ compared with ‘science pull’ is also likely to be an underlying factor, as well as the substantial time required to operationalize technical capabilities into systems that are sufficiently scaled and accessible for users. We provide recommendations for the development of AI in drug discovery with the aim of increasing its translational relevance. For example, benchmarking studies of AI tools in drug discovery need to move on from model validation and instead focus on their ability to improve decision making.

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    Subjects

    • Drug discovery and development
    • Computational models

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    Acknowledgements

    Research in the A.B. group is supported by the Center for Biotechnology, Khalifa University of Science and Technology (KU-BTC), Khalifa University and PNRR grant no. 760066/23.05.2023, cod 83/15.11.2022. V. Curean is thanked for discussions about the manuscript.

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    A.B. is a shareholder or options holder of or consultant with Healx, Pharmenable Therapeutics, Pangea Bio, Cresset Group, Evolvus, Harmonic Discovery, Ignota Labs, Turbine, VALID, Daimedex, EtioMap, Apheris, AstraZeneca and Novartis. M.C.T. has no competing interests to declare. J.W.S. is director and shareholder of JW Scannell Analytics, director and shareholder of Etheros Pharmaceuticals, holds equity options in Ochro Bio and has an advisory role with Hiro Capital. D.A.S. has no competing interests to declare. G.M.G. is a shareholder in AstraZeneca, Vertex and Nvidia. J.G.G. has no competing interests to declare. L.-L.P. has no competing Interests to declare. R.D.J. is CSO and cofounder of Powerhouse Biology and owns shares in the company. K.H. is an employee of Meiji Seika Pharma Co. and is a shareholder of Teijin Pharma. M.H. is an employee of Tanabe Pharma Corporation. S.S. is an employee of Human Chemical Company. M.M. has no competing interests to declare. M.F.S. is an employee and shareholder of biotx.ai. T.A. is CEO and cofounder of VALID and owns shares in the company. F.G. has no competing interests to declare. I.C.-C. has no competing interests to declare.

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    Bender, A., Thomas, M.C., Scannell, J.W. et al. Artificial intelligence in drug discovery — what it is, where we stand and the path forward.
    Nat Rev Drug Discov (2026). https://doi.org/10.1038/s41573-026-01496-2

    • Version of record:07 August 2026

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      :https://doi.org/10.1038/s41573-026-01496-2

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