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The world has come to a sudden halt due to the incessant spread of a viral pneumonia dubbed COVID-19 caused by the beta-coronavirus, SARS-CoV-2 The pandemic spread of the virus has already claimed lakhs of valuable lives and has infected millions of people across the globe The situation is further worsened by the fact that there is no approved therapeutics currently available for the treatment of the disease The only way to handle the crisis is the rapid development of a therapeutic strategy to combat the virus Computational biology offers resources to rapidly identify novel drug leads and to repurpose existing drugs at the expense of minimal resources and time The main protease of SARS-CoV-2 is key to the replication and propogation of the virus in the host cells Inhibiting the protease blocks replication and hence is an attractive therapeutic target in the virus The crystal structures of the protein in complex with inhibitors are available in public databases Here we describe the screening of the DrugBank database, a public repository for small molecule therapeutics, to identify approved or experimental phase drugs that can be repurposed against the main protease of SARS-CoV2 The initial screening was performed on more than 13,000 drug entries in the target database using an energy optimised pharmacophore hypothesis AARRR A sub-set of the molecules selected based on the fitness score was further screened using molecular docking by sequentially filtering the molecules through the high throughput virtual screening, extra precision and standard precision docking modalities The most promising hits were subjected to binding free energy estimation using the MMGBSA method Approved drugs viz, Cobicistat, Larotrectiniand Simeprevir were identified as potential candidates for repurposing Drugs in the discovery phase identified as inhibitors include the known cysteine protease inhibitors, Calpain inhibitor IV and an experimental cathepsin F inhibitor
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Repurposing Simeprevir, Calpain Inhibitor IV and a Cathepsin F Inhibitor Against SARS-CoV-2: A Study Using in Silico Pharmacophore Modeling and Docking Methods
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