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The transformational role of GPU computing and deep learning in drug discovery

Nature Machine Intelligence volume 4, pages 211–221 (2022)Cite this article

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Abstract

Deep learning has disrupted nearly every field of research, including those of direct importance to drug discovery, such as medicinal chemistry and pharmacology. This revolution has largely been attributed to the unprecedented advances in highly parallelizable graphics processing units (GPUs) and the development of GPU-enabled algorithms. In this Review, we present a comprehensive overview of historical trends and recent advances in GPU algorithms and discuss their immediate impact on the discovery of new drugs and drug targets. We also cover the state-of-the-art of deep learning architectures that have found practical applications in both early drug discovery and consequent hit-to-lead optimization stages, including the acceleration of molecular docking, the evaluation of off-target effects and the prediction of pharmacological properties. We conclude by discussing the impacts of GPU acceleration and deep learning models on the global democratization of the field of drug discovery that may lead to efficient exploration of the ever-expanding chemical universe to accelerate the discovery of novel medicines.

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Fig. 1: CADD workflow.
Fig. 2: Parallelization of DL architectures in single- and multi-GPU environments.
Fig. 3: Timeline of the complexity of biological systems that could be simulated with molecular dynamics.
Fig. 4: Architectures of several popular neural networks.

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Acknowledgements

This work was funded by the Canadian Institutes of Health Research (CIHR), Canadian 2019 Novel Coronavirus (2019-nCoV) Rapid Research grant numbers OV3-170631 and VR3-172639, and generous donations for COVID-19 research from TELUS, Teck Resources, the 625 Powell Street Foundation, the Tai Hung Fai Charitable Foundation and the Vancouver General Hospital Foundation. F.G. is supported by fellowships from the Canadian Institutes for Health Research (MFE-171324), Michael Smith Foundation for Health Research/VCHRI and VGH UBC Hospital Foundation (RT-2020-0408) and the Ermenegildo Zegna Foundation.

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  1. These authors contributed equally: Mohit Pandey, Michael Fernandez.

Authors and Affiliations

  1. Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada

    Mohit Pandey, Michael Fernandez, Francesco Gentile & Artem Cherkasov

  2. Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA

    Olexandr Isayev

  3. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Alexander Tropsha

  4. Nvidia Corporation, Santa Clara, CA, USA

    Abraham C. Stern

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Correspondence to Abraham C. Stern or Artem Cherkasov.

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A.C.S. is employed by the NVIDIA corporation, a manufacturer of GPU technology. No other authors received funding from NVIDIA for this work.

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Pandey, M., Fernandez, M., Gentile, F. et al. The transformational role of GPU computing and deep learning in drug discovery. Nat Mach Intell 4, 211–221 (2022). https://doi.org/10.1038/s42256-022-00463-x

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