2-dimensional quantitative structure-activity relationship modeling study of Glycine/N-methyl-D-aspartate antagonist inhibition: Genetic function approximation vis-a-vis multiple linear regression methods

被引：0

作者：

Sapre, Nitin S. ^{[1
]}

Pancholi, Nilanjana ^{[1
]}

Gupta, Swagata ^{[2
]}

Sikarwar, Arun ^{[3
]}

机构：

[1] Shri GS Inst Technol & Sci, Dept Appl Chem, Indore 452001, Madhya Pradesh, India

[2] Govt PG Coll, Dept Chem, Mhow, Madhya Pradesh, India

[3] Holkar Sci Coll, Dept Chem, Holkar, Madhya Pradesh, India

来源：

ACTA CHIMICA SLOVENICA | 2007年 / 54卷 / 04期

关键词：

QSAR; NMDA; GFA; MLR; Wiener index; Randic index; Balaban index;

D O I：

暂无

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

A comparative study of genetic function approximation (GFA) and multiple linear regression analysis(MLR) techniques for understanding 2D quantitative structure-activity relationship (2D-QSAR) on N-methyl-D-aspartate (NMDA) inhibitors was conducted using distance and connectivity based topological indices (Wiener, Balaban and Randic Indices). Models generated were used to predict the inhibitory activity for a set of test compounds. The results indicated that the GFA method proved to be superior of the two in developing 2D QSAR model in all the cases (Uni- as well as multi-variate). Individual topological indices have also been studied to understand their correlation potential. In all the cases (Wiener, Balaban and Randic), the results gave a high value of correlation (R-2 > 0.80, Q(2) > 0.79) for the GFA method while the MLR method yielded poor correlation (R-2 < 0.60 and Q(2) < 0.55). Among the three indices, Randic connectivity index proved to be the best in describing the 2D-QSAR for this series of NMDA inhibitors (R-2 = 0.893, Q(2) = 0.880, F-ratio = 216.393)

引用

页码：797 / 804

页数：8