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Author: 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
Title: GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research
  • Cord-id: lokyztp7
  • Document date: 2020_7_6
  • ID: lokyztp7
    Snippet: 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 m
    Document: 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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