Bulletin of the Korean Chemical Society

  • 제34권6호
  • /
  • Pages.1643-1650
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  • 2013
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  • 0253-2964(pISSN)
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  • 1229-5949(eISSN)
대한화학회 (Korean Chemical Society)
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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)
  • 투고 : 2012.11.26
  • 심사 : 2013.03.06
  • 발행 : 2013.06.20
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초록

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.

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피인용 문헌

  1. Beyond model interpretability using LDA and decision trees for α‐amylase and α‐glucosidase inhibitor classification studies vol.94, pp.1, 2013, https://doi.org/10.1111/cbdd.13518