A Computational Pipeline to Identify Potential Drug Targets and Interacting Chemotypes in SARS-CoV-2

Minimizing the human and economic costs of the COVID-19 pandemic and of future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a unique, computational pipeline for the rapid identification and characterization of binding sites in the proteins of novel viruses as well as the core chemical components with which these sites interact. We combine molecular-level structural modeling of proteins with clustering and cheminformatic techniques in a computationally efficient manner. Similarities between our results, experimental data, and other computational studies provide support for the effectiveness of our predictive framework. While we present here a demonstration of our tool on SARS-CoV-2, our process is generalizable and can be applied to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate homology models can be constructed. ### Competing Interest Statement The authors have declared no competing interest.