QSAR Study of Arylsulfonylpiperazine Inhibitors of 11β-HSD1 by GA-MLR, GA-PLS and GA-ANN

被引：0

作者：

Nekoei, Mehdi ^{[1
]}

Goudarzi, Nasser ^{[2
]}

Nekoei, Saeeid ^{[2
]}

Mohammadhosseini, Majid ^{[1
]}

机构：

[1] Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood

[2] Faculty of Chemistry, Shahrood University of Technology, P.O. Box 316, Shahrood

来源：

Analytical Chemistry Letters | 2014年 / 4卷 / 01期

关键词：

11β-HSD1; ANN; Arylsulfonylpiperazine; MLR; PLS; QSAR;

D O I：

10.1080/22297928.2013.856167

中图分类号：

学科分类号：

摘要：

The main goal of this study is to establish robust theoretical models for prediction of pIC values related to arylsulfonylpiperazines. After drawing the molecular structures, a suitable set of molecular descriptors that fulfill the best fitted models were selected using genetic algorithm. Three models were proposed for prediction of experimental results involving multiple linear regression (MLR), partial least square (PLS) and artificial neural network (ANN) techniques. The accuracy of the suggested models was confirmed by cross-validation, validation through an external test set, and Y-randomization. Numerical values predicted by all the three models are in good agreement with the experimental ones. However, the results derived from ANN technique show better compatibility (R2 =0.930, R2 =0.788, RMSE =0.137, RMSE =0.320, Ftrain=72.850, Ftest=1.743) with the actual experimental values. © 2014 Har Krishan Bhalla & Sons.

引用

页码：14 / 28

页数：14

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