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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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