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.
Keywords: Aldose reductase (AR) inhibitors, Polylol pathway, Quantitative structure-activity relationship (QSAR), Multiple linear regression (MLR), Genetic algorithm (GA), cell membranes, alcohol, glomerulus tissues, peripheral nerve, flavonoids
Medicinal Chemistry
Title:Quantitative Structure-Activity Relationship Study of Aromatic Inhibitors Against Rat Lens Aldose Reductase Activity Using Variable Selections
Volume: 9 Issue: 3
Author(s): Mankil Jung, Yongnam Lee, Minjoo Shim, Eunyoung Lim, Eun Jig Lee and Hyun Chul Lee
Affiliation:
Keywords: Aldose reductase (AR) inhibitors, Polylol pathway, Quantitative structure-activity relationship (QSAR), Multiple linear regression (MLR), Genetic algorithm (GA), cell membranes, alcohol, glomerulus tissues, peripheral nerve, flavonoids
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.
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Cite this article as:
Jung Mankil, Lee Yongnam, Shim Minjoo, Lim Eunyoung, Jig Lee Eun and Chul Lee Hyun, Quantitative Structure-Activity Relationship Study of Aromatic Inhibitors Against Rat Lens Aldose Reductase Activity Using Variable Selections, Medicinal Chemistry 2013; 9 (3) . https://dx.doi.org/10.2174/1573406411309030012
DOI https://dx.doi.org/10.2174/1573406411309030012 |
Print ISSN 1573-4064 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6638 |
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Carbohydrates are the most essential organic molecules and are involved in the maintenance of various physiological and metabolic processes in living organisms. Carbohydrate-based compounds have come to the attention of researchers because of their significant contributions to biological functions, such as cell development and cell proliferation, connections between several cells, ...read more
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