Statistical tools for virtual screening

被引：9

作者：

Krumrine, JR ^{[1
]}

Maynard, AT ^{[1
]}

Lerman, CL ^{[1
]}

机构：

[1] AstraZeneca Pharmaceut LP, Computat Chem & Informat, Dept Chem, Wilmington, DE 19850 USA

来源：

JOURNAL OF MEDICINAL CHEMISTRY | 2005年 / 48卷 / 23期

关键词：

D O I：

10.1021/jm0501026

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

In large-scale virtual screening (VS) campaigns, data are often computed for millions of compounds to identify leads, but there remains the task of prioritizing VS "hits" for experimental assays and the dilemma of assessing true/false positives. We present two statistical methods for mining large databases: (1) a general scoring metric based on the VS signal-to-noise level within a compound neighborhood; (2) a neighborhood-based sampling strategy for reducing database size, in lieu of property-based filters.

引用

页码：7477 / 7481

页数：5

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