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

Nafiseh       Vahedi; Majid       Mohammadhosseini; Mehdi       Nekoei
doi:10.2174/1573411016999200518083359
Abstract

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.
Keywords: Artificial Neural Networks (ANN), genetic algorithm, Multiple Linear Regressions (MLR), Poly(ADP-ribose) polymerases (PARPs) inhibitors, QSAR, Support Vector Machine (SVM).
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Graphical Abstract

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DOI https://dx.doi.org/10.2174/1573411016999200518083359	Print ISSN 1573-4110
Publisher Name Bentham Science Publisher	Online ISSN 1875-6727
Current Analytical Chemistry

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

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