Keeping pace with the explosive growth of chemical libraries with structure-based virtual screening

Abstract

Recent efforts to synthetically expand drug-like chemical libraries have led to the emergence of unprecedently large virtual databases. This surge of make-on-demand molecular datasets has been received enthusiastically across the drug discovery community as a new paradigm. In several recent studies, virtual screening (VS) of larger make-on-demand collections resulted in the identification of novel molecules with higher potency and specificity compared to more conventional VS campaigns relying on smaller in-stock libraries. These results inspired ultra-large VS against various clinically relevant targets, including key proteins of the SARS-CoV-2 virus. As library sizes rapidly surpassed the billion compounds mark, new computational screening strategies emerged, shifting from conventional docking to fragment-based and machine learning-accelerated methods. These approaches significantly reduce computational demands of ultra-large screenings by lowering the number of molecules explicitly docked onto a target. Such strategies already demonstrated promise in evaluating libraries of tens of billions of molecules at relatively low computational cost. Herein, we review recent advancements in structure-based methods for ultra-large virtual screening that drug discovery practitioners have adopted to explore the ever-expanding chemical universe.

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