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Global coronavirus disease pandemic (COVID-19) caused by newly identifiedSARS- CoV-2 coronavirus continues to claim the lives of thousands of peopleworldwide. The unavailability of specific medications to treat COVID-19 has ledto drug repositioning efforts using various approaches, including computationalanalyses. Such analyses mostly rely on molecular docking and require the 3Dstructure of the target protein to be available. In this study, we utilized aset of machine learning algorithms and trained them on a dataset ofRNA-dependent RNA polymerase (RdRp) inhibitors to run inference analyses onantiviral and anti-inflammatory drugs solely based on the ligand information.We also performed virtual screening analysis of the drug candidates predictedby machine learning models and docked them against the active site of SARS-CoV-2 RdRp, a key component of the virus replication machinery. Based on theligand information of RdRp inhibitors, the machine learning models were able toidentify candidates such as remdesivir and baloxavir marboxil, molecules withdocumented activity against RdRp of the novel coronavirus. Among the otheridentified drug candidates were beclabuvir, a non-nucleoside inhibitor of thehepatitis C virus (HCV) RdRp enzyme, and HCV protease inhibitors paritaprevirand faldaprevir. Further analysis of these candidates using molecular dockingagainst the SARS-CoV-2 RdRp revealed low binding energies against the enzymeactive site. Our approach also identified anti-inflammatory drugs lupeol,lifitegrast, antrafenine, betulinic acid, and ursolic acid to have potentialactivity against SARS-CoV-2 RdRp. We propose that the results of this study areconsidered for further validation as potential therapeutic options againstCOVID-19.

Predicting inhibitors for SARS-CoV-2 RNA-dependent RNA polymerase using machine learning and virtual screening