Computational strategies towards developing novel SARS-CoV-2 M^pro inhibitors against COVID-19.

ABSTRACT: The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 M^pro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 M^pro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA

SUBMITTER: Luo D 

PROVIDER: S-EPMC8398673 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature