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Annual Review of Biomedical Data Science

Volume 6, 2023

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Abstract

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein–protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning–based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

Keyword(s): deep learning, drug discovery, free energy simulations, GPU acceleration, machine learning, molecular docking, structure-based virtual screenings, ultralarge libraries
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/content/journals/10.1146/annurev-biodatasci-020222-025013
2023-08-10
2024-05-05
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/content/journals/10.1146/annurev-biodatasci-020222-025013
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Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches
Annual Review of Biomedical Data Science 6, 229 (2023); https://doi.org/10.1146/annurev-biodatasci-020222-025013
/content/journals/10.1146/annurev-biodatasci-020222-025013
/content/journals/10.1146/annurev-biodatasci-020222-025013
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