The DFT-B3LYP method, with the base set 6-31G (d), was used to calculate several quantum chemical descriptors of 44 substituted flavonoids. The best descriptors were selected to establish the quantitative structure activity relationship (QSAR) of the inhibitory activity against aldose reductase using principal components analysis (PCA), multiple regression analysis (MLR), nonlinear regression (RNLM) and an artificial neural network (ANN). We propose a quantitative model according to these analyses, and we interpreted the activity of the compounds based on the multivariate statistical analysis. This study shows that the MLR and MNLR predict activity, but compared to the results of the ANN model, we conclude that the predictions achieved by the latter are more effective and better than the other models. The results indicate that the ANN model is statistically significant and shows very good stability toward data variation for the validation method. The contribution of each descriptor to the structure-activity relationship was also evaluated.
The DFT-B3LYP method, with the base set 6-31G (d) was used to calculate electronic and charge descriptors. The present study was performed using principal component analysis (PCA), multiple linear regression analysis (MLR) and non-linear multiple regression analysis (MNLR) to predict unambiguous QSAR models of 46 substituted phenethylamines toward psychotomimetic activity. Results showed that the MLR and MNLR predict activity in a satisfactory manner. But among those models, we concluded that the latter one provides a better agreement between calculated and observed values of psychotomimetic activity. Also it shows very good stability towards data variations for the validation methods.
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