Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods

被引：69

作者：

Deng, W

Breneman, C ^{[1
]}

Embrechts, MJ

机构：

[1] Rensselaer Polytech Inst, Dept Chem, Troy, NY 12180 USA

[2] Rensselaer Polytech Inst, Dept Decis Sci & Engn Syst, Troy, NY 12180 USA

来源：

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES | 2004年 / 44卷 / 02期

关键词：

D O I：

10.1021/ci034246+

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Inspired by the concept of knowledge-based scoring functions, a new quantitative structure-activity relationship (QSAR) approach is introduced for scoring protein-ligand interactions. This approach considers that the strength of ligand binding is correlated with the nature of specific ligand/binding site atom pairs in a distance-dependent manner. In this technique, atom pair occurrence and distance-dependent atom pair features are used to generate an interaction score. Scoring and pattern recognition results obtained using Kernel PLS (partial least squares) modeling and a genetic algorithm-based feature selection method are discussed.

引用

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页码：699 / 703

页数：5

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