Quantitative structure-activity relationship study of aromatic inhibitors against rat lens aldose reductase activity using variable selections

Mankil Jung, Yongnam Lee, Minjoo Shim, Eunyoung Lim, Eun Jig Lee, Hyun Chul Lee

Research output: Contribution to journalArticle

Abstract

A quantitative structure-activity relationship (QSAR) study of aromatic inhibitors against aldose reductase (AR) activity was performed using variable selection from stepwise multiple linear regression (MLR) and genetic algorithm (GA)-MLR. As a result of variable selection, stepwise MLR and GA-MLR gave the same results with one, two, three and five descriptors and different results with four and six descriptors. GA-MLR produced higher values and was better in explanatory and predictive power than stepwise MLR in four variables. AR activity (pIC50) of aromatic derivatives was expressed with acceptable explanatory (74.6-81.2%) and predictive power (68.8-74.4%) in models 3 and 4. The resulting models with the given descriptors illustrate that hydrophobic and electrostatic interactions play a significant role in inhibition of AR activity. This study suggests that the QSAR models can be used as guidelines to predict improved aldose reductase inhibitory activity and to obtain reliable predictions in structurally diverse compounds.

Original languageEnglish
Pages (from-to)410-419
Number of pages10
JournalMedicinal Chemistry
Volume9
Issue number3
DOIs
Publication statusPublished - 2013 May 1

All Science Journal Classification (ASJC) codes

  • Drug Discovery

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