GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Conference · Tue Sep 01 04:00:00 EDT 2020

DOI:https://doi.org/10.1145/3388440.3412472· OSTI ID:1775240

LeGrand, Scott ^[1]; Scheinberg, Aaron ^[2]; Tillack, Andreas F. ^[3]; Thavappiragasam, Mathialakan ^[4]; Vermaas, Josh ^[4]; Agarwal, Rupesh ^[4]; Larkin, Jeff ^[1]; Poole, Duncan ^[1]; Santos-Martins, Diogo ^[3]; Solis-Vasquez, Leonardo ^[5]; Koch, Andreas ^[5]; Forli, Stefano ^[3]; Hernandez Mendoza, Oscar ^[4]; Smith, Jeremy ^[4]; Sedova, Ada ^[4]

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1. NVIDIA, Santa Clara, CA
2. Princeton Plasma Physics Laboratory (PPPL)
3. Scripps Research Institute, La Jolla, CA
4. ORNL
5. Darmstadt University of Technology, Germany

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1775240
• Country of Publication: United States
• Language: English