Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

Acharya, Atanu; Agarwal, Rupesh; Baker, Matthew; Baudry, Jerome; Bhowmik, Debsindhu; Boehm, Swen; Byler, Kendall; Coates, Leighton; Chen, Sam Yen-Chi; Cooper, Connor J.; Demerdash, Omar; Daidone, Isabella; Eblen, John; Ellingson, Sally R.; Forli, Stefano; Glaser, Jens; Gumbart, James C.; Gunnels, John; Hernandez, Oscar; Irle, Stephan; Larkin, Jeffery; Lawrence, Travis J; LeGrand, Scott; Liu, Shih-Hsien; Mitchell, Julie C.; Park, Gilchan; Parks, Jerry M.; Pavlova, Anna; Petridis, Loukas; Poole, Duncan; Pouchard, Line; Ramanathan, Arvind; Rogers, David; Santos-Martins, Diogo; Scheinberg, Aaron; Sedova, Ada; Shen, Shawn; Smith, Jeremy C.; Smith, Micholas; Soto, Carlos; Tsaris, Aristides; Thavappiragasam, Mathialakan; Tillack, Andreas F.; Vermaas, Josh V; Vuong, Van Quan; Yin, Junqi; Yoo, Shinjae; Zahran, Mai; Zanetti-Polzi, Laura

We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.