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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