Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

The current pandemic
demands a search for therapeutic agents against
the novel coronavirus SARS-CoV-2. Here, we present an efficient computational
strategy that combines machine learning (ML)-based models and high-fidelity
ensemble docking studies to enable rapid screening of possible therapeutic
ligands. Targeting the binding affinity of molecules for either the
isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human
ACE2 interface complex, we screen ligands from drug and biomolecule
data sets that can potentially limit and/or disrupt the host–virus
interactions. Top scoring one hundred eighty-seven ligands (with 75
approved by the Food and Drug Administration) are further validated
by all atom docking studies. Important molecular descriptors (2χn, topological surface
area, and ring count) and promising chemical fragments (oxolane, hydroxy,
and imidazole) are identified to guide future experiments. Overall,
this work expands our knowledge of small-molecule treatment against
COVID-19 and provides a general screening pathway (combining quick
ML models with expensive high-fidelity simulations) for targeting
several chemical/biochemical problems.