Yan Xin, Li Jiabo, Gu Qiong, Xu Jun
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, 132 East Circle at University City, Guangzhou, Guangdong, 510006, China.
J Comput Chem. 2014 Jun 5;35(15):1122-30. doi: 10.1002/jcc.23603. Epub 2014 Apr 12.
Virtual screening of a large chemical library for drug lead identification requires searching/superimposing a large number of three-dimensional (3D) chemical structures. This article reports a graphic processing unit (GPU)-accelerated weighted Gaussian algorithm (gWEGA) that expedites shape or shape-feature similarity score-based virtual screening. With 86 GPU nodes (each node has one GPU card), gWEGA can screen 110 million conformations derived from an entire ZINC drug-like database with diverse antidiabetic agents as query structures within 2 s (i.e., screening more than 55 million conformations per second). The rapid screening speed was accomplished through the massive parallelization on multiple GPU nodes and rapid prescreening of 3D structures (based on their shape descriptors and pharmacophore feature compositions).
对大型化学文库进行虚拟筛选以鉴定药物先导物，需要搜索/叠加大量三维（3D）化学结构。本文报道了一种图形处理单元（GPU）加速的加权高斯算法（gWEGA），该算法可加快基于形状或形状特征相似性得分的虚拟筛选。使用86个GPU节点（每个节点有一张GPU卡），gWEGA可以在2秒内筛选出源自整个ZINC类药物数据库的1.1亿个构象，以多种抗糖尿病药物作为查询结构（即每秒筛选超过5500万个构象）。快速筛选速度是通过在多个GPU节点上进行大规模并行化以及对3D结构进行快速预筛选（基于其形状描述符和药效团特征组成）来实现的。
