Self-Organizing Maps for In Silico Screening and Data Visualization

被引：21

作者：

Digles, Daniela ^{[1
]}

Ecker, Gerhard F. ^{[1
]}

机构：

[1] Univ Vienna, Dept Med Chem, A-1090 Vienna, Austria

来源：

MOLECULAR INFORMATICS | 2011年 / 30卷 / 10期

关键词：

Self-organizing maps; Kohonen maps; Virtual screening; Chemoinformatics; Computational chemistry; DRUG DISCOVERY; NEURAL-NETWORKS; CLASSIFICATION; INHIBITORS; IDENTIFICATION; PREDICTION; QSAR; DESCRIPTORS; EXPLORATION; UNIVERSE;

D O I：

10.1002/minf.201100082

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

Self-organizing maps, which are unsupervised artificial neural networks, have become a very useful tool in a wide area of disciplines, including medicinal chemistry. Here, we will focus on two applications of self-organizing maps: the use of self-organizing maps for in silico screening and for clustering and visualisation of large datasets. Additionally, the importance of parameter selection is discussed and some modifications to the original algorithm are summarised.

引用

页码：838 / 846

页数：9

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