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Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

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Abstract

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

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Authors and Affiliations

  1. Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA

    Alexander H. Williams & Chang-Guo Zhan

  2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA

    Alexander H. Williams & Chang-Guo Zhan

  3. GSK Upper Providence, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA

    Alexander H. Williams

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  1. Alexander H. Williams
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Conceptualization, CG Zhan; Data curation and analysis, AH Williams; Writing – original draft preparation, AH Williams; Writing – review and editing, AH Williams and CG Zhan; Funding acquisition, CG Zhan. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by the funding of the Molecular Modeling and Biopharmaceutical Center at the University of Kentucky College of Pharmacy and the National Science Foundation (Directorate for Mathematical and Physical Sciences, NSF Grant DMS-2245903 under funding opportunity NSF 22-600—Joint DMS/NIGMS Initiative to Support Research at the Interface of the Biological and Mathematical Sciences).

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Williams, A.H., Zhan, CG. Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management. BioDrugs 37, 649–674 (2023). https://doi.org/10.1007/s40259-023-00611-8

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