计算机科学
高斯分布
网格
结合位点
数据挖掘
药物发现
任务(项目管理)
人工智能
算法
模式识别(心理学)
化学
数学
生物信息学
工程类
生物
生物化学
几何学
计算化学
系统工程
作者
Joel Graef,Christiane Ehrt,Matthias Rarey
标识
DOI:10.1021/acs.jcim.3c00336
摘要
Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.
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