Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

In
response to the ongoing COVID-19 pandemic, there is a worldwide
effort being made to identify potential anti-SARS-CoV-2 therapeutics.
Here, we contribute to these efforts by building machine-learning
predictive models to identify novel drug candidates for the viral
targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA
polymerase (RdRp). Chemist-curated training sets of substances were
assembled from CAS data collections and integrated with curated bioassay
data. The best-performing classification models were applied to screen
a set of FDA-approved drugs and CAS REGISTRY substances that are similar
to, or associated with, antiviral agents. Numerous substances with
potential activity against 3CLpro or RdRp were found, and some were
validated by published bioassay studies and/or by their inclusion
in upcoming or ongoing COVID-19 clinical trials. This study further
supports that machine learning-based predictive models may be used
to assist the drug discovery process for COVID-19 and other diseases.