Discovery of SARS-CoV-2 Main Protease Inhibitors from Natural
Products via Machine Learning with Pharmacophore Modeling, SimilarityMethods, and Molecular Dynamics
Discovery of SARS-CoV-2 Main Protease Inhibitors from Natural
Products via Machine Learning with Pharmacophore Modeling, SimilarityMethods, and Molecular DynamicsMohammad A. Khanfar
2025 · DOI: 10.2174/0115734080371991250605114818
Current Enzyme Inhibition · 0 Citations
TLDR
Two rounds of molecular docking were applied to filter and refine the hits, leading to the identification of 17 promising compounds with favorable binding interactions and physicochemical profiles, and stable binding within the Mpro active site.
Abstract
Two rounds of molecular docking were applied to filter and refine the hits, leading to the identification of 17 promising compounds with favorable binding interactions and physicochemical profiles. Molecular dynamics simulations of top hits demonstrated stable binding within the Mpro active site, with binding energies supporting their potential as potent inhibitors.