Rapid Prediction of Possible Inhibitors for SARS-CoV-2 Main Protease using Docking and FPL Simulations


Appearance for the first time from Wuhan, China, the SARS-CoV-2 rapidly outbreaks worldwide and causes a serious global health issue. The effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this work, we have tried to rapidly predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations adopted good consistent results with the respective experiment with correlation coefficients of R_Dock=0.72 ± 0.14 and R_W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors, which were selected from a ZINC15 sub-database, for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. The obtained results probably lead to enhance COVID-19 therapy.