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Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Nature Reviews Drug Discovery volume 23, pages 141–155 (2024)Cite this article

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Abstract

Quantitative structure–activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term ‘deep QSAR’. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.

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Fig. 1: Contrasting traditional and deep QSAR models.
Fig. 2: Generative molecular design.
Fig. 3: Workflow for deep docking.
Fig. 4: Molecular simulations enhanced by deep learning potentials in the calculation of ligand binding affinity.

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Acknowledgements

The authors acknowledge support of their studies by the National Institutes of Health (grant R01GM140154) for A.T. and National Science Foundation (grant CHE-2154447) for O.I.

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Authors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Alexander Tropsha

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Olexandr Isayev

  3. University of Strasbourg, Strasbourg, France

    Alexandre Varnek

  4. ETH, Zurich, Switzerland

    Gisbert Schneider

  5. University of British Columbia, Vancouver, BC, Canada

    Artem Cherkasov

  6. Photonic Inc., Coquitlam, BC, Canada

    Artem Cherkasov

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Tropsha, A., Isayev, O., Varnek, A. et al. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 23, 141–155 (2024). https://doi.org/10.1038/s41573-023-00832-0

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