  Global coronavirus disease pandemic (COVID-19) caused by newly identified
SARS- CoV-2 coronavirus continues to claim the lives of thousands of people
worldwide. The unavailability of specific medications to treat COVID-19 has led
to drug repositioning efforts using various approaches, including computational
analyses. Such analyses mostly rely on molecular docking and require the 3D
structure of the target protein to be available. In this study, we utilized a
set of machine learning algorithms and trained them on a dataset of
RNA-dependent RNA polymerase (RdRp) inhibitors to run inference analyses on
antiviral and anti-inflammatory drugs solely based on the ligand information.
We also performed virtual screening analysis of the drug candidates predicted
by 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 the
ligand information of RdRp inhibitors, the machine learning models were able to
identify candidates such as remdesivir and baloxavir marboxil, molecules with
documented activity against RdRp of the novel coronavirus. Among the other
identified drug candidates were beclabuvir, a non-nucleoside inhibitor of the
hepatitis C virus (HCV) RdRp enzyme, and HCV protease inhibitors paritaprevir
and faldaprevir. Further analysis of these candidates using molecular docking
against the SARS-CoV-2 RdRp revealed low binding energies against the enzyme
active site. Our approach also identified anti-inflammatory drugs lupeol,
lifitegrast, antrafenine, betulinic acid, and ursolic acid to have potential
activity against SARS-CoV-2 RdRp. We propose that the results of this study are
considered for further validation as potential therapeutic options against
COVID-19.
