Computational discovery of SARS-CoV-2 main protease inhibitors via a virtual screening, molecular docking, molecular dynamics and MM/PBSA calculation-driven approach

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 2019 (COVID-19), has evoked a global pandemic. Due to its rapid transmission rate and the severity of illness, the urgent need for the expedited design and development of effective therapeutics is evident. Computer-aided drug design (CADD) methods have been employed to accelerate the drug development process. More than 30 000 medication compounds were subjected to virtual screening for the SARS-CoV-2 main protease. The top 10 molecules based on the binding affinity scores were chosen and subjected to extra-precision docking and their pharmacokinetic properties were explored to validate whether they bound well to the SARS-CoV-2 main protease. The results indicated that the binding free energy values between Mpro and these ligands predominantly fall within the range of −7 to −8 kcal mol⁻¹, suggesting relatively stable interactions between the ligands and the protein target. Significant contributions to the binding of most small molecules were identified through molecular dynamics simulations and MM/PBSA (molecular mechanics/Poisson–Boltzmann surface area) analyses, with residues such as His164, Glu166, and Asp187 being found to be crucial. Therefore, these residues have been recognized as potential targets for drug design. In summary, ZINC000306568896 exhibited the optimal binding free energy of −28.68 kcal mol⁻¹ and was evaluated as the lead compound with the strongest binding affinity in this series. Its favourable pharmacokinetic properties and its stable association with the active site suggest that it is a promising lead inhibitor for SARS-CoV-2. These results demonstrate that this ligand has great potential to be an ideal lead inhibitor for SARS-CoV-2 and to expedite the development of therapeutic interventions against COVID-19.