Evolutionary neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors

被引：10

作者：

Kyngas, J ^{[1
]}

Valjakka, J ^{[1
]}

机构：

[1] UNIV JOENSUU,DEPT CHEM,FIN-80101 JOENSUU,FINLAND

来源：

QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS | 1996年 / 15卷 / 04期

关键词：

evolutionary neural networks; genetic algorithms; QSAR;

D O I：

10.1002/qsar.19960150404

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

The evolutionary neural network (ENN) is a new system for modelling multifactor data. The strength of ENN's are that they can extract insignificant predictors, choose the size of the hidden layer and fine tune the parameters needed in training the network. We have used an ENN to predict the biological activities of Dihydrofolate Reductase Inhibitors. As a result, we found that evolutionary neural networks give more accurate predictions than statistical methods and feedforward neural networks.

引用

页码：296 / 301

页数：6

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