GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research
Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations i...
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creator | LeGrand, Scott Scheinberg, Aaron Tillack, Andreas F. Thavappiragasam, Mathialakan Vermaas, Josh V. Agarwal, Rupesh Larkin, Jeff Poole, Duncan Santos-Martins, Diogo Solis-Vasquez, Leonardo Koch, Andreas Forli, Stefano Hernandez, Oscar Smith, Jeremy C. Sedova, Ada |
description | Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic. |
doi_str_mv | 10.1145/3388440.3412472 |
format | Conference Proceeding |
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subjects | Applied computing -- Life and medical sciences -- Computational biology |
title | GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research |
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