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

Predictive modelling of the LD50 activities of coumarin derivatives using neural statistical approaches: Electronic descriptor-based DFT

A study of structure–activity relationship (QSAR) was performed on a set of 30 coumarin-based molecules. This study was performed using multiple linear regressions (MLRs) and an artificial neural network (ANN). The predicted values of the antioxidant activities of coumarins were in good agreement with the experimental results. Several statistical criteria, such as the mean square error (MSE) and the correlation coefficient (R), were studied to evaluate the developed models. The best results were obtained with a network architecture [8-4-1] (R = 0.908, MSE = 0.032), activation functions (tansig–purelin) and the Levenberg–Marquardt learning algorithm. The model proposed in this study consists of large electronic descriptors that are used to describe these molecules. The results suggested that the proposed combination of calculated parameters may be useful for predicting the antioxidant activities of coumarin derivatives

Biological activities of triazine derivatives. Combining DFT and QSAR results

In order to investigate the relationship between activities and structures, a 3D-QSAR study is applied to a set of 43 molecules based on triazines. This study was conducted using the principal component analysis (PCA) method, the multiple linear regression method (MLR) and the artificial neural network (ANN). The predicted values of activities are in good agreement with the experimental results. The artificial neural network (ANN) techniques, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.9 with an 8-3-1 ANN model which is a good result. As a result of quantitative structure–activity relationships, we found that the model proposed in this study is constituted of major descriptors used to describe these molecules. The obtained results suggested that the proposed combination of several calculated parameters could be useful to predict the biological activity of triazine derivatives