A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds

Abstract

The new pandemic caused by the coronavirus (SARS-CoV-2) has become the biggest challenge that the world is facing today. It has been creating a devastating global crisis, causing countless deaths and great panic. The search for an effective treatment remains a global challenge owing to controversies related to available vaccines. A great research effort (clinical, experimental, and computational) has emerged in response to this pandemic, and more than 125000 research reports have been published in relation to COVID-19. The majority of them focused on the discovery of novel drug candidates or repurposing of existing drugs through computational approaches that significantly speed up drug discovery. Among the different used targets, the SARS-CoV-2 main protease (M^pro), which plays an essential role in coronavirus replication, has become the preferred target for computational studies. In this review, we examine a representative set of computational studies that use the M^pro as a target for the discovery of small-molecule inhibitors of COVID-19. They will be divided into two main groups, structure-based and ligand-based methods, and each one will be subdivided according to the strategies used in the research. From our point of view, the use of combined strategies could enhance the possibilities of success in the future, permitting to development of more rigorous computational studies in future efforts to combat current and future pandemics.

Keywords: COVID-19; Computational; Coronavirus; Main protease inhibitor; Pandamic; SARS-CoV-2.