Quantitative structure activity relationship for the computational prediction of α-glucosidase inhibitory

Kraim, Khairedine; Khatmi, Djameleddin; Saihi, Youcef; Ferkous, Fouad; Brahimi, Méziane

doi:10.1016/j.chemolab.2009.03.006

Cited by 19 publications

(14 citation statements)

References 24 publications

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“…12,13 However, a limited number of studies have been devoted to the search for new α-glucosidase inhibitors using QSAR. [14][15][16] Many different chemometrics methods, such as Multiple Linear Regression (MLR), different types of Neural Networks (NN), Genetic Algorithms (GAs) can be employed to derive correlation models between molecular structures and properties.…”

Section: -11mentioning

confidence: 99%

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

Saihi¹,

Kraim²,

Ferkous³

et al. 2013

Bulletin of the Korean Chemical Society

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A non linear QSAR model was constructed on a series of 57 xanthone and curcuminoide derivatives as α-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 α-glucosidase inhibitory.

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Section: -11mentioning

confidence: 99%

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

Saihi¹,

Kraim²,

Ferkous³

et al. 2013

Bulletin of the Korean Chemical Society

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“…29 Of the dihydroxyxanthones, 1,3-dihydroxyxanthone possesses antimalarial activity, 30 antihypertensive activity 31 and can inhibit α-glucosidase. 27,28 The same has been reported for 1,3-diacetoxyxanthone and in addition, this compound can inhibit Na/K-ATPase. 32 Likewise, its analogue, 1,6-dihydroxyxanthone, possesses antihypertensive activity 31 and can inhibit α-glucosidase.…”

Section: Introductionmentioning

confidence: 56%

“…19 Acetylation of hydroxy xanthones and thioxanthones can further enhance their biological activities. 23,24 Monohydroxyxanthones, such as 1-hydroxyxanthone, are reported in the literature as monoamine oxidase inhibitors, 25,26 α-glucosidase inhibitors 27,28 and exhibit antioxidant properties. 29 Of the dihydroxyxanthones, 1,3-dihydroxyxanthone possesses antimalarial activity, 30 antihypertensive activity 31 and can inhibit α-glucosidase.…”

Section: Introductionmentioning

confidence: 99%

“…32 Likewise, its analogue, 1,6-dihydroxyxanthone, possesses antihypertensive activity 31 and can inhibit α-glucosidase. 27,28 Trihydroxyxanthones, like 1,3,5-trihydroxyxanthone, has shown anticancer 33 and antimalarial activity 30 . The same compound tested in vitro exhibited antiviral activity on HIV-1-infected MT-4 cells and MDCK cells infected with influenza virus.…”

Section: Introductionmentioning

confidence: 99%

“…34 Trihydroxyxanthones have also been shown to be monoamine oxidase 25,26 and Na/K-ATPase inhibitors. 32 Finally, 1,3,7-trihydroxyxanthone also shows anticancer activity 33 and possesses monoamine oxidase, 25,26 α-glucosidase 27,28 and fatty acid synthase inhibitory activity. 35 Synthetically, xanthones are usually obtained by chemical synthesis from benzophenones or diaryl ethers under harsh reaction conditions and/or in the presence of strong acids or toxic metals.…”

Section: Introductionmentioning

confidence: 99%

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An efficient and convenient microwave-assisted chemical synthesis of (thio)xanthones with additional in vitro and in silico characterization

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Prediction of the binding affinities of adenosine A2A receptor antagonists based on the heuristic method and support vector machine

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Support vector machine (SVM) was used to develop a nonlinear quantitative structure-activity relationship (QSAR) model for the prediction of the activities of the adenosine A 2A receptor antagonists. Six molecular descriptors selected by the heuristic method (HM) in CODESSA were used as inputs for SVM. The results obtained by SVM were compared with those obtained by HM. The mean squared errors (MSEs) for the training set given by HM and SVM are 0.08 and 0.05, respectively, which shows the performance of SVM model is better than that of the HM model.

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Quantitative structure activity relationship for the computational prediction of α-glucosidase inhibitory

Cited by 19 publications

References 24 publications

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

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

An efficient and convenient microwave-assisted chemical synthesis of (thio)xanthones with additional in vitro and in silico characterization

Prediction of the binding affinities of adenosine A2A receptor antagonists based on the heuristic method and support vector machine

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