Discovery of Potent Quinazolinone Inhibitors Against SARS‐CoV‐2 Mpro via an Integrated Computational Strategy

ABSTRACT The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 remains a primary target for antiviral drug discovery. In this study, an integrated computational workflow—comprising molecular docking, triplicate 200 ns molecular dynamics (MD) simulations, Molecular Mechanics–Generalized Born Surface Area (MM/GBSA) binding free energy calculations, and free energy landscape (FEL) analysis—was employed to evaluate potential Mpro inhibitors. Five compounds exhibited higher predicted affinities than the crystallographic reference inhibitor 3W, with compounds 9g and 11e emerging as the most promising candidates. MD simulations demonstrated that both ligands enhanced the structural stability of Mpro, as evidenced by low root‐mean‐square deviation (RMSD) values, reduced root‐mean‐square fluctuation (RMSF) in key regions, and the maintenance of persistent hydrogen‐bond networks. Notably, compound 9g maintained stable interactions with the critical catalytic residue Glu166. MM/GBSA analysis yielded favorable binding free energies (Δ G bind ) of −38.84 kcal/mol for 9g and −32.52 kcal/mol for 11e, while FEL mapping confirmed their capacity to stabilize the protease in deep, energetically favorable conformational minima. Furthermore, the computational predictions correlated strongly with experimental IC50 data, validating the robustness of the applied workflow. These results provide molecular‐level insights into the potent inhibitory activity of 9g and 11e, identifying them as viable lead scaffolds for the development of optimized anti‐SARS‐CoV‐2 therapeutics.