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Estimating Pesticide Field Half-lives from a Backpropagation Neural Network

 

作者: D. Domine,   J. Devillers,   M. Chastrette,   W. Karcher,  

 

期刊: SAR and QSAR in Environmental Research  (Taylor Available online 1993)
卷期: Volume 1, issue 2-3  

页码: 211-219

 

ISSN:1062-936X

 

年代: 1993

 

DOI:10.1080/10629369308028829

 

出版商: Taylor & Francis Group

 

关键词: field half-life;pesticides;neural networks;correspondence factor analysis;discriminant factor analysis;structure-activity relationships

 

数据来源: Taylor

 

摘要:

The field half-lives of 110 pesticides were modelled using a backpropagation neural network (NN). The molecules were described by means of the frequency of 17 structural fragments. Before training the NN, different scaling transformations were assayed. Best results were obtained with correspondence factor analysis which also allowed a reduction of dimensionality. The training and testing sets of the NN analysis gave 95.5% and 84.6% of good classifications, respectively. Comparison with discriminant factor analysis showed that a backpropagation NN was more appropriate to model the field half-lives of pesticides.

 

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