利用pmemdGTI实现快速准确的结合亲和力预测：GPU加速热力学积分的高效实现
Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration.
作者信息
Lee Tai-Sung, Hu Yuan, Sherborne Brad, Guo Zhuyan, York Darrin M
机构信息
Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology, Rutgers University , Piscataway, New Jersey 08854, United States.
Department of Chemistry, Modeling and Informatics, Merck Research Laboratories, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States.
出版信息
J Chem Theory Comput. 2017 Jul 11;13(7):3077-3084. doi: 10.1021/acs.jctc.7b00102. Epub 2017 Jun 23.
Abstract
We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.
摘要
我们报告了在图形处理器（GPU）上的AMBER 16软件包的pmemd模块上实现热力学积分方法（pmemdGTI）。对于配体-蛋白质结合亲和力的计算，pmemdGTI代码相对于单个CPU核心通常能实现超过两个数量级的加速，且无统计学上显著的数值差异，因此为药物发现应用提供了一个强大的新工具。
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