Toward a principled methodology for neural network design and performance evaluation in QSAR. Application to the prediction of LogP
被引:44
作者:
Duprat, AF
论文数: 0引用数: 0
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机构:Ecole Super Phys & Chim Ind, Elect Lab, F-75231 Paris 05, France
Duprat, AF
Huynh, T
论文数: 0引用数: 0
h-index: 0
机构:Ecole Super Phys & Chim Ind, Elect Lab, F-75231 Paris 05, France
Huynh, T
Dreyfus, G
论文数: 0引用数: 0
h-index: 0
机构:Ecole Super Phys & Chim Ind, Elect Lab, F-75231 Paris 05, France
Dreyfus, G
机构:
[1] Ecole Super Phys & Chim Ind, Elect Lab, F-75231 Paris 05, France
[2] Ecole Super Phys & Chim Ind, Rech Organ Lab, F-75231 Paris, France
来源:
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
|
1998年
/
38卷
/
04期
关键词:
D O I:
10.1021/ci980042v
中图分类号:
O6 [化学];
学科分类号:
0703 ;
摘要:
The prediction of properties of molecules from their structure (QSAR) is basically a nonlinear regression problem. Neural networks are proven to be parsimonious universal approximators of nonlinear functions; therefore, they are excellent candidates for performing the nonlinear regression tasks involved in QSAR. However, their full potential can be exploited only in the framework of a rigorous approach. In the present paper, we describe a principled methodology for designing neural networks for QSAR and estimating their performances, and we apply this approach to the prediction of logP. We compare our results to those obtained on the same molecules by other methods.