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

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