gpu加速药物发现与巅峰超级计算机对接:COVID-19研究的移植、优化和应用

S. Legrand, A. Scheinberg, A. F. Tillack, M. Thavappiragasam, J. Vermaas, Rupesh Agarwal, J. Larkin, D. Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli, Oscar R. Hernandez, Jeremy C. Smith, A. Sedova
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引用次数: 42

摘要

蛋白质配体对接是一种在药物发现过程中用于筛选潜在药物化合物与给定蛋白质受体结合能力的计算机工具。实验药物筛选成本高、耗时长,希望以高通量的方式进行大规模对接计算,缩小实验搜索空间。现有的计算对接工具很少考虑到高性能计算。因此,优化以最大限度地利用领导级计算设施中可用的高性能计算资源,使这些设施能够用于药物发现。在这里,我们介绍了AutoDock-GPU程序在Summit超级计算机上的移植、优化和验证,并将其应用于针对导致当前COVID-19大流行的SARS-CoV-2病毒蛋白的初始化合物筛选工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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