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Molecules (Basel, Switzerland) Nov 2020Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is...
Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure-Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (M) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the M of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the M enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.
Topics: Amobarbital; Antiviral Agents; Binding Sites; Computer Simulation; Coronavirus 3C Proteases; Drug Discovery; Drug Repositioning; Humans; Machine Learning; Molecular Docking Simulation; Molecular Dynamics Simulation; Pandemics; Protease Inhibitors; Protein Binding; Quantitative Structure-Activity Relationship; SARS-CoV-2; Small Molecule Libraries; Software; Thermodynamics; Thyroxine; COVID-19 Drug Treatment
PubMed: 33172092
DOI: 10.3390/molecules25215172