A 3D-QSAR CoMSIA study on 3-azolylmethylindoles as anti-leishmanial agents

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

Background:

The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes.

Methods:

In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors.

Results:

The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities.

Conclusion:

Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Three-dimensional quantitative structure-activity relationship and docking studies in a series of anthocyanin derivatives as cytochrome P450 3A4 inhibitors

The cytochrome P450 (CYP)3A4 enzyme affects the metabolism of most drug-like substances, and its inhibition may influence drug safety. Modulation of CYP3A4 by flavonoids, such as anthocyanins, has been shown to inhibit the mutagenic activity of mammalian cells. Considering the previous investigations addressing CYP3A4 inhibition by these substances, we studied the three-dimensional quantitative structure–activity relationship (3D-QSAR) in a series of anthocyanin derivatives as CYP3A4 inhibitors. For the training dataset (n=12), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) yielded crossvalidated and non-crossvalidated models with a q² of 0.795 (0.687) and r² of 0.962 (0.948), respectively. The models were also validated by an external test set of four compounds with r² of 0.821 (CoMFA) and r² of 0.812 (CoMSIA). The binding affinity modes associated with experimentally derived IC₅₀ (half maximal inhibitory concentration) values were confirmed by molecular docking into the CYP3A4 active site with r² of 0.66. The results obtained from this study are useful for a better understanding of the effects of anthocyanin derivatives on inhibition of carcinogen activation and cellular DNA damage.