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

被引：31

作者：

LeGrand, Scott ^{[1
]}

Scheinberg, Aaron ^{[2
]}

Tillack, Andreas F. ^{[3
]}

Thavappiragasam, Mathialakan ^{[4
]}

Vermaas, Josh V. ^{[4
]}

Agarwal, Rupesh ^{[5
]}

Larkin, Jeff ^{[1
]}

Poole, Duncan ^{[1
]}

Santos-Martins, Diogo ^{[3
]}

Solis-Vasquez, Leonardo ^{[6
]}

Koch, Andreas ^{[6
]}

Forli, Stefano ^{[3
]}

Hernandez, Oscar ^{[4
]}

Smith, Jeremy C. ^{[5
,7
]}

Sedova, Ada ^{[4
]}

机构：

[1] NVIDIA Corp, Santa Clara, CA USA

[2] Jubilee Dev, Cambridge, MA USA

[3] Scripps Res, La Jolla, CA USA

[4] Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA

[5] Univ Tennessee, Knoxville, TN USA

[6] Tech Univ Darmstadt, Darmstadt, Germany

[7] UT ORNL, Oak Ridge, TN USA

来源：

ACM-BCB 2020 - 11TH ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS | 2020年

基金：

美国国家卫生研究院;

关键词：

Drug discovery; high-performance computing; GPU acceleration; protein-ligand docking; MOLECULAR DOCKING; AUTOMATED DOCKING; SYSTEM;

D O I：

10.1145/3388440.3412472

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：10

共 37 条

[1] The Protein Data Bank [J].