Computational identification of novel antiviral leads against SARS-CoV-2 Mpro through systematic virtual screening and molecular dynamics study

Despite advances in vaccines and supportive therapies, a targeted treatment for COVID-19 remains lacking, prompting researchers to explore specific pharmacological interventions to fill this gap. In this quest, we targeted the main protease (Mpro) of SARS-CoV-2, a well-established druggable target crucial for viral replication and transcription. 714 antiviral compounds were computationally screened against the active-site residues of Mpro, and 17 hits showed significantly better interactions with Mpro than the selected control (N3). The two top-scoring compounds, LY2784544 and Pimobendan, were further evaluated through 100-ns molecular dynamics (MD) simulations. Comprehensive post-simulation analyses, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration, solvent-accessible surface area, principal component analysis, free energy landscape (FEL) mapping, and MM/PBSA binding free energy calculations, demonstrated stable complex formation and persistent engagement of the catalytic dyad (His41–Cys145) and S1/S2 subsites. Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) results indicated favorable binding free energies, with Pimobendan showing comparatively stronger energetic stabilization. The study identifies LY2784544 and Pimobendan as potential antiviral agents for Mpro. Given their stability, strong binding energy (BE) profiles, and existing clinical data, these compounds warrant additional experimental validation to determine their potential efficacy against SARS-CoV-2.