Gaussian process: An efficient technique to solve quantitative structure-property relationship problems

被引：10

作者：

Enot, DP ^{[1
]}

Gautier, R ^{[1
]}

Le Marouille, JY ^{[1
]}

机构：

[1] Ecole Natl Super Chim Rennes, Inst Chim Rennes, Dept Physicochim, UPRES 1795, F-35700 Rennes, France

来源：

SAR AND QSAR IN ENVIRONMENTAL RESEARCH | 2001年 / 12卷 / 05期

关键词：

1,2-dithiole-3-one; automatic relevance determination; Gaussian process; log P; neural networks;

D O I：

10.1080/10629360108035385

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

We introduce the Gaussian process (GP) model for the empirical modelling of the log P values of 44 1,2-dithiole-3-one molecules. A brief theoretical description of the method is given. Descriptive and predictive abilities of GP are evaluated and compared to multilinear regression results. Special attention is devoted to the automatic relevance determination (ARD) to reduce input variable numbers, which avoid the use of principal component analysis. The present approach was found to be an efficient method and a good alternative to more complicated using artificial neural network systems.

引用

收藏

页码：461 / 469

页数：9

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