Ligand-based virtual screening using binary kernel discrimination

被引：3

作者：

Chen, BN

Harrison, RF

Hert, J

Mpanhanga, C

Willett, P ^{[1
]}

Wilton, DJ

机构：

[1] Univ Sheffield, Krebs Inst Biomolec Res, Dept Chem, Sheffield S10 2TN, S Yorkshire, England

[2] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S10 2TN, S Yorkshire, England

[3] Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England

来源：

MOLECULAR SIMULATION | 2005年 / 31卷 / 08期

关键词：

binary kernel discrimination; circular substructure; fingerprints; similarity searching; virtual screening;

D O I：

10.1080/08927020500134177

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

This paper discusses the use of a machine- learning technique called binary kernel discrimination ( BKD) for virtual screening in drug- and pesticide- discovery programmes. BKD is compared with several other ligand- based tools for virtual screening in databases of 2D structures represented by fragment bit- strings, and is shown to provide an effective, and reasonably efficient, way of prioritising compounds for biological screening.

引用

收藏

页码：597 / 604

页数：8

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