QM/NN QSPR models with error estimation: Vapor pressure and LogP

被引：81

作者：

Beck, B ^{[1
]}

Breindl, A ^{[1
]}

Clark, T ^{[1
]}

机构：

[1] Univ Erlangen Nurnberg, Comp Chem Ctr, D-91052 Erlangen, Germany

来源：

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES | 2000年 / 40卷 / 04期

关键词：

D O I：

10.1021/ci990131n

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

QSPR models for logP and vapor pressures of organic compounds based on neural net interpretation of descriptors derived from quantum mechanical (semiempirical MO; AM1) calculations are presented. The models are cross-validated by dividing the compound set into several equal portions and training several individual multilayer feedforward neural nets (trained by the back-propagation of errors algorithm), each with a different portion as test set. The results of these nets are combined to give a mean predicted property value and a standard deviation. The performance of two models, for logP and the vapor pressure at room temperature, is analyzed, and the reliability of the predictions is tested.

引用

页码：1046 / 1051

页数：6

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