Neural Modelling of the Biodegradability of Benzene Derivatives
作者:
J. Devillers,
期刊:
SAR and QSAR in Environmental Research
(Taylor Available online 1993)
卷期:
Volume 1,
issue 2-3
页码: 161-167
ISSN:1062-936X
年代: 1993
DOI:10.1080/10629369308028827
出版商: Taylor & Francis Group
关键词: neural networks;structure-biodegradability relationships;backpropagation algorithm;benzene derivatives
数据来源: Taylor
摘要:
The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives. 127 chemicals selected from the BIODEG data bank (Syracuse Research Corporation, 1992) were described by means of 20 structural descriptors taking into account the nature and position of the substituents on the benzene ring. Three classes of biodegradability were selected and modelled from the BNN. A 20/5/3 BNN (α = 0.8 and η = 0.5) correctly classified 92% (104/113) of the training and 86% (12/14) of the testing sets. The results were compared to those produced by the BIODEG probability program (Syracuse Research Corporation, Version 2.13).
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