虚拟筛选
计算机科学
药效团
集合(抽象数据类型)
特征(语言学)
叠加原理
基础(线性代数)
Atom(片上系统)
数据挖掘
算法
人工智能
模式识别(心理学)
生物信息学
数学
几何学
生物
数学分析
语言学
哲学
嵌入式系统
程序设计语言
作者
G. Madhavi Sastry,Steven L. Dixon,Woody Sherman
摘要
Shape-based methods for aligning and scoring ligands have proven to be valuable in the field of computer-aided drug design. Here, we describe a new shape-based flexible ligand superposition and virtual screening method, Phase Shape, which is shown to rapidly produce accurate 3D ligand alignments and efficiently enrich actives in virtual screening. We describe the methodology, which is based on the principle of atom distribution triplets to rapidly define trial alignments, followed by refinement of top alignments to maximize the volume overlap. The method can be run in a shape-only mode or it can include atom types or pharmacophore feature encoding, the latter consistently producing the best results for database screening. We apply Phase Shape to flexibly align molecules that bind to the same target and show that the method consistently produces correct alignments when compared with crystal structures. We then illustrate the effectiveness of the method for identifying active compounds in virtual screening of eleven diverse targets. Multiple parameters are explored, including atom typing, query structure conformation, and the database conformer generation protocol. We show that Phase Shape performs well in database screening calculations when compared with other shape-based methods using a common set of actives and decoys from the literature.
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