In-Silico Screening, Molecular Dynamics, and DFT Analysis of ZINC and ChEMBL Library Compounds for SARS-CoV-2 Main Protease Inhibition

Abstract

Although COVID-19 is no longer classified as a global emergency, the emergence of SARS-CoV-2 variants highlights the urgent need for antiviral drug discovery. This study identifies potent inhibitors of the SARS-CoV-2 main protease, supporting future preparedness and advancing antiviral strategies. Using experimental drugs from the ZINC and ChEMBL libraries, a systematic workflow combining SwissSimilarity-based screening, molecular docking, molecular dynamics (MD) simulations, and density functional theory (DFT) calculations was employed for robust candidate assessment. Five potential inhibitors, sharing a 4-(2-pyrimidin-4-yl)-morpholine motif, were identified. Among them, Apilimod, known for its immunomodulatory properties, showed promising efficacy against viral replication in prior studies. Detailed interaction dynamics were analyzed through 2.5 microseconds of MD simulations (500 ns per complex), revealing critical insights into the stability, binding modes, and conformational dynamics of the drug-protein complexes. MM/PBSA binding free energy calculations further demonstrated Apilimod’s superior binding affinity compared to other candidates. These findings highlight the therapeutic potential of Apilimod and its structural analogs as promising SARS-CoV-2 antivirals. By leveraging advanced computational techniques, this study provides valuable insights for combating COVID-19 and addressing future viral threats.