论文标题
GPU加速药物发现与峰会超级计算机上的对接：移植，优化和应用于COVID-19
GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research
论文作者
论文摘要
蛋白质 - 配体对接是一种用于筛选潜在药物化合物的硅胶工具，其能力与药物发现运动中的蛋白质受体结合。实验性药物筛查是昂贵且耗时的，希望以高通量方式进行大规模的对接计算，以缩小实验搜索空间。现有的计算对接工具很少有人考虑到高性能计算。因此，在领导级计算设施中获得的高性能计算资源的使用以最大程度地利用了这些设施，可以利用这些设施进行药物发现。在这里，我们介绍了Summit SuperCupter的Autodock-GPU程序的移植，优化和验证，及其在初始复合筛选工作中的应用，以靶向负责当前Covid-19的SARS-COV-2病毒的蛋白质。
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.
