Machine Learning Models Identify Inhibitors of SARS-CoV-2

Gawriljuk, Victor O.; Kyaw Zin, Phyo Phyo; Foil, Daniel H.; Bernatchez, Jean; Beck, Sungjun; Beutler, Nathan; Ricketts, James; Yang, Linlin; Rogers, Thomas; Puhl, Ana C.; Zorn, Kimberley M.; Lane, Thomas R.; Godoy, Andre S.; Oliva, Glaucius; Siqueira-Neto, Jair L.; Madrid, Peter B.; Ekins, Sean

With the ongoing SARS-CoV-2 pandemic there is an urgent need for the discovery of a treatment for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need and numerous compounds have been selected for in vitro testing by several groups already. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, CPI1062 and CPI1155 showed antiviral activity in HeLa-ACE2 cell-based assays and represent potential repurposing opportunities for COVID-19. This approach can be greatly expanded to exhaustively virtually screen available molecules with predicted activity against this virus as well as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 is available at www.assaycentral.org.