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Computational Molecular Docking and Virtual Screening Revealed Promising SARS-CoV-2 Drugs
preprintsubmitted on 04.05.2020, 08:04 and posted on 06.05.2020, 05:10 by Maryam Hosseini, Wanqiu Chen, Charles Wang
The pandemic of novel coronavirus disease 2019 (COVID-19) is rampaging the world with more than 1.4 million of confirmed cases and more than 85,000 of deaths across world by April 9th, 2020. There is an urgent need to identify effective drugs to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structured proteins. Three non-structural proteins such as main protease, papain like protease, and RNA-dependent RNA polymerase are believed to play a crucial role in the virus replication. We applied a computational ligand-receptor binding modeling and performed a comprehensive virtual screening on the FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina. Our computational studies indicated that Simeprevir, Ledipasvir, Idarubicin, Saquinavir, Ledipasivir, Partitaprevir, Glecaprevir, and Velpatasvir are all promising inhibitors, which displayed a lower binding energy (higher inhibitory effect) than Remdesivir, Lopinavir, and Ritonavir. However, we found that chloroquine and hydroxychloroquine, which showed efficacy in treating the COVID-19 in recent clinical studies, had high binding energy with all three proteins, suggesting they may work through a different mechanism. We also identified several novel drugs as potential inhibitors against SARS-CoV-2, including antiviral Raltegravir; antidiabetic Amaryl; antibiotics Retapamulin, Rifimixin, and Rifabutin; antiemetic Fosaprepitant and Netupitant. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 drugs that may be considered for further clinical studies.