In silico discovery of multi-targeting inhibitors for the COVID-19 treatment by molecular docking, molecular dynamics simulation studies, and ADMET predictions

Coronavirus disease-2019 (COVID-19), a global pandemic, has currently infected more than 247 million people around the world. Nowadays, several receptors of COVID-19 have been reported, and few of them are explored for drug discovery. New mutant strains of COVID-19 are emerging since the first outbreak of disease and causing significant morbidity and mortality across the world. Although, few drugs were approved for emergency uses, however, promising drug with well-proven clinical efficacy is yet to be discovered. Hence, researchers are continuously attempting to search for potential drug candidates targeting the well-established enzymatic targets of the virus. The present study aims to discover the antiviral compounds as potential inhibitors against the five targets in various stages of the SARS-CoV-2 life cycle, i.e., virus attachments (ACE2 and TMPRSS2), viral replication, and transcription (M-pro, PLpro and RdRp), using the most reliable molecular docking and molecular dynamics method. The ADMET study was then carried out to determine the pharmacokinetics and toxicity of several compounds with the best docking results. To provide a more effective mechanism for demonstrating protein-ligand interactions, molecular docking data were subjected to a molecular dynamic (MD) simulation at 300 K for 100 ns. In terms of structural stability, structure compactness, solvent accessible surface area, residue flexibility, and hydrogen bond interactions, the dynamic features of complexes have been compared.

Automated summary

This study aims to discover antiviral compounds as potential inhibitors against different stages of the SARS-CoV-2 life cycle using molecular docking and molecular dynamics methods. Protein-ligand interactions were analyzed through molecular docking data and dynamic simulations to evaluate the dynamic features of the complexes.