Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV‑2 Target Antagonists and Inhibitors Using Machine Learning

A unique approach
to bioactivity and chemical data curation coupled
with random forest analyses has led to a series of target-specific
and cross-validated predictive feature fingerprints (PFF) that have
high predictability across multiple therapeutic targets and disease
stages involved in the severe acute respiratory syndrome due to coronavirus
2 (SARS-CoV-2)-induced COVID-19 pandemic, which include plasma kallikrein,
human immunodeficiency virus (HIV)-protease, nonstructural protein
(NSP)­5, NSP12, Janus kinase (JAK) family, and AT-1. The approach was
highly accurate in determining the matched target for the different
compound sets and suggests that the models could be used for virtual
screening of target-specific compound libraries. The curation-modeling
process was successfully applied to a SARS-CoV-2 phenotypic screen
and could be used for predictive bioactivity estimation and prioritization
for clinical trial selection; virtual screening of drug libraries
for the repurposing of drug molecules; and analysis and direction
of proprietary data sets.