In silico tuning of binding selectivity for new SARS-CoV-2 main protease inhibitors

Abstract

Rapid identification of effective SARS-CoV-2 inhibitors is essential for managing the ongoing pandemic and preparing for future outbreaks. This study aims to develop an efficient computational framework to accelerate pre-screening and optimization of inhibitors through functional group modifications of FDA-approved drugs, Adrafinil and Baicalein, targeting the SARS-CoV-2 main protease (MPro). We introduce MDBinding, a computational drug optimization program designed to enhance the inhibitor screening process by integrating molecular dynamics (MD) simulations. MDBinding systematically identifies inhibitors with improved binding affinities to MPro through functional group modifications, refining lead compound design. Combined with the previously developed PerQMConf module, MDBinding provides a robust in silico framework for rapid drug discovery. This approach significantly reduces the time and cost of inhibitor development while identifying promising candidates for experimental validation. The findings highlight the potential of MDBinding to accelerate antiviral drug discovery and improve the efficiency of computational drug design.