  The world has witnessed unprecedented human and economic loss from the
COVID-19 disease, caused by the novel coronavirus SARS-CoV-2. Extensive
research is being conducted across the globe to identify therapeutic agents
against the SARS-CoV-2. Here, we use a powerful and efficient computational
strategy by combining machine learning (ML) based models and high-fidelity
ensemble docking simulations to enable rapid screening of possible therapeutic
molecules (or ligands). Our screening is based on the binding affinity to
either the isolated SARS-CoV-2 S-protein at its host receptor region or to the
Sprotein-human ACE2 interface complex, thereby potentially limiting and/or
disrupting the host-virus interactions. We first apply our screening strategy
to two drug datasets (CureFFI and DrugCentral) to identify hundreds of ligands
that bind strongly to the aforementioned two systems. Candidate ligands were
then validated by all atom docking simulations. The validated ML models were
subsequently used to screen a large bio-molecule dataset (with nearly a million
entries) to provide a rank-ordered list of ~19,000 potentially useful compounds
for further validation. Overall, this work not only expands our knowledge of
small-molecule treatment against COVID-19, but also provides an efficient
pathway to perform high-throughput computational drug screening by combining
quick ML surrogate models with expensive high-fidelity simulations, for
accelerating the therapeutic cure of diseases.
