The inhibitory activity of aldose reductase of flavonoid compounds: Combining DFT and QSAR calculations

Ghamali, Mounir; Chtita, Samir; Hmamouchi, Rachid; Adad, Azeddine; Bouachrine, Mohammed; Lakhlifi, Tahar

doi:10.1016/j.jtusci.2015.09.006

Cited by 27 publications

(16 citation statements)

References 26 publications

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“…Authors concluded that overall toxicity of the phenol derivatives involves a balance between electron density on the aromatic ring and lipophilicity. Also, electron withdrawing substituents induced toxicity, but much less than their electron donating counterparts (Selassie et al, 1998;Ghamali et al, 2015).…”

Section: Introductionmentioning

confidence: 98%

Theoretical study of a series of phenol derivatives: molecular properties vs. cytotoxicity

Michalík

Sádecká

Lukeš

2017

Acta Chimica Slovaca

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The quantum chemical calculations using DFT were performed for 2-alkyl-4X and 2,6-dialkyl-4-X substituted phenols. Based on the optimal geometries the bond dissociation enthalpies (BDEs), proton enthalpies (PAs) and the lipophilicities were computed. Additionally, simple geometry parameter was found correlating well with experimental leukemia cell toxicity of substituted phenols. Next, we have found no linear dependence between PA or BDE values and log1/C values in gas phase or in water despite the radical toxicity mechanism proposed in the literature.

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Section: Introductionmentioning

confidence: 98%

Theoretical study of a series of phenol derivatives: molecular properties vs. cytotoxicity

Michalík

Sádecká

Lukeš

2017

Acta Chimica Slovaca

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“…MLR can also be used to select descriptors to be used as input parameters in MNLR PLS and ANN. Multiple linear regression, partial least squares (PLS) and multiple non-linear regression models (MLR) were generated to predict anticancer activities and pIC50 [26][27][28][29] using the software XLSTAT (2015 version) [30] and the ANN was generated using the software Matlab.…”

Section: Discussionmentioning

confidence: 99%

“…Though there are neither theoretical nor empirical rules to definitively determine the number of quiet layers or the number of neuron layers required to generate an optimal ANN, one hidden layer seems to be sufficient for most chemical applications of ANN. Some authors [27,32] have offered a parameter to be used to calculate the optimal number of hidden neurons, which plays a major role in determining the best ANN architecture. ρ is defined as follows: ρ = (Number of data points in the training set/ Sum of the number of connections in the ANN).…”

Section: Discussionmentioning

confidence: 99%

3D-QSAR models to predict anti-cancer activity on a series of protein P38 MAP kinase inhibitors

Hadaji

Bourass

Ouammou

et al. 2017

Journal of Taibah University for Science

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Protein kinases are essential components of various signaling pathways and represent attractive targets for therapeutic interventions. Kinase inhibitors are currently used to treat malignant tumors, as well as autoimmune diseases, due to their involvement in immune cell signaling. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Multiple Non-Linear Regression (MNLR), Artificial Neural Network (ANN) and cross-validation analyses, were performed on a set of P38 MAP kinases as anti-cancer agents. This method, which is based on molecular modeling (molecular mechanics, Hartree-Fock (HF)), was used to determine the structural parameters, electronic properties, and energy associated with the molecules we examined. MLR, PLS, and MNLR analyses were performed on 46 protein P38 MAP kinase analogs to determine the relationships between molecular descriptors and the anti-cancer properties of the P38 MAP kinase analogs. The MLR model was validated by the external validation and standardization approach. The ANN, given the descriptors obtained from the MLR, exhibited a correlation coefficient close to 0.94. The predicted model was confirmed by two methods, leave-one-out (LOO) cross-validation and scrambling (or Y-randomization). We observed a high correlation between predicted and experimental activity, thereby both validating and demonstrating the high quality of the QSAR model that we described.

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“…Principal component analysis (PCA) [37] was conducted to identify the link among different variables. Table 3 presents the correlation matrix that represents the correlations among the fourteen descriptors.…”

Section: Principal Component Analysismentioning

confidence: 99%

Combining DFT and QSAR computation to predict the interaction of flavonoids with the GABA (A) receptor using electronic and topological descriptors

Ghamali

Chtita

Aouidate

et al. 2017

Journal of Taibah University for Science

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To establish a quantitative structure-activity relationship model of the binding affinity constants (−log K i) of 41 flavonoid derivatives towards the GABA (A) receptor, the DFT-B3LYP method with basis set 6-31G (d) was performed to gain insights into the chemical structure and property information for the studied compounds. The best topological and electronic descriptors were selected. This work was conducted with principal component analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN). According to these analyses, we propose quantitative models and interpret the activity of the compounds based on multivariate statistical analysis. The statistical results of the MLR, MNLR and ANN indicate that the determination coefficients R 2 were 0.896, 0.925 and 0.916, respectively. The results show that the three modelling methods can predict the studied activity well and may be useful for predicting the biological activity of new compounds. The statistical results indicate that the models are statistically significant and stable with data variation in the external validation.

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The inhibitory activity of aldose reductase of flavonoid compounds: Combining DFT and QSAR calculations

Cited by 27 publications

References 26 publications

Theoretical study of a series of phenol derivatives: molecular properties vs. cytotoxicity

Theoretical study of a series of phenol derivatives: molecular properties vs. cytotoxicity

3D-QSAR models to predict anti-cancer activity on a series of protein P38 MAP kinase inhibitors

Combining DFT and QSAR computation to predict the interaction of flavonoids with the GABA (A) receptor using electronic and topological descriptors

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