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

Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack, Mathialakan Thavappiragasam, Josh V. Vermaas, Rupesh Agarwal, Jeff Larkin, Duncan Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli, Oscar Hernandez, Jeremy C. Smith, Ada Sedova
NVIDIA Corporation, Santa Clara, California
arXiv:2007.03678 [q-bio.BM], (6 Jul 2020)

@misc{legr2020gpuaccelerated,

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

   author={Scott LeGrand and Aaron Scheinberg and Andreas F. Tillack and Mathialakan Thavappiragasam and Josh V. Vermaas and Rupesh Agarwal and Jeff Larkin and Duncan Poole and Diogo Santos-Martins and Leonardo Solis-Vasquez and Andreas Koch and Stefano Forli and Oscar Hernandez and Jeremy C. Smith and Ada Sedova},

   year={2020},

   eprint={2007.03678},

   archivePrefix={arXiv},

   primaryClass={q-bio.BM}

}

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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.
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