Application of Kohonen Neural Networks in Classification of Biologically Active Compounds
作者:
D.B. Kirew,
J.R. Chretien,
P. Bernard,
F. Ros,
期刊:
SAR and QSAR in Environmental Research
(Taylor Available online 1998)
卷期:
Volume 8,
issue 1-2
页码: 93-107
ISSN:1062-936X
年代: 1998
DOI:10.1080/10629369808033262
出版商: Taylor & Francis Group
关键词: Kohonen Neural Networks;classification;model validation;pesticide database
数据来源: Taylor
摘要:
Automated data classification is an indispensable tool in Drug Design. It allows to select homogeneous training sets or to distinguish compounds with required biological properties. The Kohonen Neural Networks (KNN) suggest new means for classification of biologically interesting compounds. In this paper, first, capabilities of KNN in data dimensionality reduction are presented as compared with the capabilities of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The advantages of KNN become evident with increasing data dimensionality and size of the training set. Then, new methods are suggested to evaluate the quality of KNN models. Finally, a case study on chemical and biological data is presented. The database studied includes more than 2000 organophosphorous potent pesticides. The Kohonen maps were obtained which allow to distinguish compounds with different biological behaviour.
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