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http://dx.doi.org/10.5012/bkcs.2013.34.6.1643

Nonlinear QSAR Study of Xanthone and Curcuminoid Derivatives as α-Glucosidase Inhibitors  

Saihi, Youcef (Department of Chemistry, Faculty of Sciences, University of Badji Mokhtar)
Kraim, Khairedine (Department of Chemistry, Faculty of Sciences, University of Badji Mokhtar)
Ferkous, Fouad (Department of Chemistry, Faculty of Sciences, University of Badji Mokhtar)
Djeghaba, Zeineddine (Department of Chemistry, Faculty of Sciences, University of Badji Mokhtar)
Azzouzi, Abdelkader (Department of Chemistry, Faculty of Sciences, University of Djelfa)
Benouis, Sabrina (Department of Chemistry, Faculty of Sciences, University of Badji Mokhtar)
Publication Information
Bulletin of the Korean Chemical Society / v.34, no.6, 2013 , pp. 1643-1650 More about this Journal
Abstract
A non linear QSAR model was constructed on a series of 57 xanthone and curcuminoide derivatives as ${\alpha}$-glucosidase inhibitors by back-propagation neural network method. The neural network architecture was optimized to obtain a three-layer neural network, composed of five descriptors, nine hidden neurons and one output neuron. A good predictive determination coefficient was obtained (${R^2}_{Pset}$ = 86.7%), the statistical results being better than those obtained with the same data set using a multiple regression analysis (MLR). As in the MLR model, the descriptor MATS7v weighted by Van der Waals volume was found as the most important independent variable on the ${\alpha}$-glucosidase inhibitory.
Keywords
${\alpha}$-Glucosidase; Inhibitors; Xanthone-curcuminoide derivatives; QSAR; Artificial neural networks;
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