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The world has witnessed unprecedented human and economic loss from theCOVID-19 disease, caused by the novel coronavirus SARS-CoV-2. Extensiveresearch is being conducted across the globe to identify therapeutic agentsagainst the SARS-CoV-2. Here, we use a powerful and efficient computationalstrategy by combining machine learning (ML) based models and high-fidelityensemble docking simulations to enable rapid screening of possible therapeuticmolecules (or ligands). Our screening is based on the binding affinity toeither the isolated SARS-CoV-2 S-protein at its host receptor region or to theSprotein-human ACE2 interface complex, thereby potentially limiting and/ordisrupting the host-virus interactions. We first apply our screening strategyto two drug datasets (CureFFI and DrugCentral) to identify hundreds of ligandsthat bind strongly to the aforementioned two systems. Candidate ligands werethen validated by all atom docking simulations. The validated ML models weresubsequently used to screen a large bio-molecule dataset (with nearly a millionentries) to provide a rank-ordered list of ~19,000 potentially useful compoundsfor further validation. Overall, this work not only expands our knowledge ofsmall-molecule treatment against COVID-19, but also provides an efficientpathway to perform high-throughput computational drug screening by combiningquick ML surrogate models with expensive high-fidelity simulations, foraccelerating the therapeutic cure of diseases.

Screening of Therapeutic Agents for COVID-19 using Machine Learning and Ensemble Docking Simulations