Recent advances in computational drug discovery for therapy against coronavirus SARS-CoV-2

Abstract

Despite essential experimental efforts focused on studying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), computational chemistry methods are promising complementary tools in combating coronavirus disease 2019 (COVID-19). The present review aims to provide readers with the recent progress and advances in computational approaches currently used to streamline drug discovery and development in the context of COVID-19. Our review is dual purpose. It is intended (a) to familiarize the readership with the general concept of QSAR, in silico screening, molecular docking and molecular dynamics (MD) simulations and (b) to provide key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. We outline how structure- and ligand-based drug design can accelerate the structural elucidation of pharmacological drug targeting and the discovery of preclinical drug candidate molecules. Several examples of MD computational studies demonstrate how atomistic MD simulations can facilitate our understanding of the molecular basis of drug actions and biological mechanisms of virus inhibition in atomic detail. Finally, the short- and long-term perspectives in computational drug discovery are discussed. The aim of this study is to summarize the last three years' progress and advances in computational approaches currently used to streamline the drug discovery and development process in the context of COVID-19. Materials and methods. The literature overview of QSAR, in silico screening, machine learning, molecular docking and molecular dynamics (MD) simulations is given in the context of COVID-19. The literature search was performed using online databases, such as Scopus, Web of Science, PDB-protein databank, and PubMed, focusing on the following keywords - human coronavirus, QSAR, molecular docking, virtual screening, machine learning, molecular dynamics, Mpro and PLpro proteases, SARS-CоV-2, respectively. Results. The review familiarizes the readership with the general concept of QSAR, in silico screening, machine learning, molecular docking and MD simulations and provides key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. Conclusions. New insight into the recent progress and achievements in computer-guided drug discovery for therapeutic agents against SARS-CoV-2 is provided