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

revised on 09.08.2020, 17:39 and posted on 10.08.2020, 08:44 by Pham Minh Quan, Khanh B. Vu, T. Ngoc Han Pham, Le Thi Thuy Huong, Linh Hoang Tran, Nguyen Thanh Tung, Van Vu, Trung Hai Nguyen, Son Tung Ngo
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.


This work was supported by Vietnam National Foundation for Science & Technology Development (NAFOSTED) grant #104.99-2019.57.


Email Address of Submitting Author


Ton Duc Thang University



ORCID For Submitting Author


Declaration of Conflict of Interest

No Conflict of Interest