  Structure-based Deep Fusion models were recently shown to outperform several
physics- and machine learning-based protein-ligand binding affinity prediction
methods. As part of a multi-institutional COVID-19 pandemic response, over 500
million small molecules were computationally screened against four protein
structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19.
Three enhancements to Deep Fusion were made in order to evaluate more than 5
billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion
concept was refined by formulating the architecture as one, coherently
backpropagated model (Coherent Fusion) to improve binding-affinity prediction
accuracy. Secondly, the model was trained using a distributed, genetic
hyper-parameter optimization. Finally, a scalable, high-throughput screening
capability was developed to maximize the number of ligands evaluated and
expedite the path to experimental evaluation. In this work, we present both the
methods developed for machine learning-based high-throughput screening and
results from using our computational pipeline to find SARS-CoV-2 inhibitors.
