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

For this month’s computational chemistry paper of the month, we have chosen to highlight a recent review of the computational approaches used to identify inhibitors of the SARS-CoV-2 Main Protease (MPro).

In this review, the authors helpfully compile a set of studies that have utilised modern computational approaches to identify and aid the design of potential new anti-SARS-CoV-2 drugs. The described studies used a range of structure-based and ligand-based approaches; including docking, molecular dynamics, free-energy perturbations, and 2D and 3D machine learning/ QSAR models. Importantly, this review emphasises the need for multi pronged design approaches that utilise a combination of modelling strategies to enhance understanding and decision-making in drug discovery. For example, molecular dynamics simulations are a helpful tool to corroborate the stability of docked ligand poses, since the performance of the main docking software for non-covalent MPro inhibitors was reported at only 26%. Another key learning that has been demonstrated by this review, is the importance of the availability of data on the SARS-CoV MPro and its known inhibitors when the COVID-19 pandemic hit. The SARS-CoV and SARS-CoV-2 main proteases have a high homology that enabled model-building and inhibitor prediction at an early stage of the COVID-19 pandemic. Looking to the future, the wealth of data and models that have been produced in the study of SARS-CoV-2 over the past years will provide a useful start point for tackling potential new coronavirus outbreaks with anti-viral compounds.

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