Discovery of Promising Inhibitors for SARS-CoV-2 Therapeutic Targets: A Quantum Mechanics and Molecular Modeling study

Abstract

SARS-CoV-2 triggers a worldwide medical crisis, affecting the world's social, emotional, physical, and economic equilibrium. However, treatment choices and targets for finding a solution to COVID-19's threat are becoming limiting. A viable approach to combating the threat of COVID-19 is to create pharmacological and therapeutic targets by uncovering promising inhibitors and proteins in the viral life cycle of COVID-19. Therefore, in this study, we employ high-throughput virtual screening to screen library of phytocompounds from selected medicinal plants targeting SARS-CoV-2 spike glycoprotein, 3CLpro, PLpro and RdRp. Virtual screening approach have been reported as a promising method to identify biologically active compounds from large libraries. In addition, molecular docking, Prime MM/GBSA (molecular mechanics/generalized born surface area) analysis, molecular dynamics (MD) simulation and pharmacokinetic/drug-likeness model was performed to identify potential phytodrugs. In support to these approaches, we employ series of chemical quantum calculation, semi empirical Hamiltonian and density functional theory analysis to develop therapeutic agents against COVID-19. We propose that the discover therapeutic targets/molecules may open the way for the establishment of phytodrugs for the management of COVID-19 pandemics and new chemical libraries for preventing COVID-19 entry into the host, based on the findings of this integrated computational investigation.