Status of HTS data mining approaches

被引：30

作者：

Böcker, A

Schneider, G

Teekentrup, A ^{[1
]}

机构：

[1] Boehringer Ingelheim Pharma GMBH & Co KG, Dept Lead Discovery, D-88397 Biberach, Germany

[2] Univ Frankfurt, Inst Organ Chem & Chem Biol, D-60439 Frankfurt, Germany

来源：

QSAR & COMBINATORIAL SCIENCE | 2004年 / 23卷 / 04期

关键词：

classification; feature extraction; library design; machine leaning; molecular descriptor; similarity; virtual screening;

D O I：

10.1002/qsar.200330860

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

High-throughput screening of large compound collections results in large sets of data. This review gives an overview of the most frequently employed computational techniques for the analysis of such data and the establishment of first QSAR models. Various methods for descriptor selection, classification and data mining are discussed. Recent trends include the application of kernel-based machine learning methods for the design of focused libraries and compilation of target-family biased compound collections.

引用

收藏

页码：207 / 213

页数：7

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