Journal of Chemical and Pharmaceutical Research (ISSN : 0975-7384)

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Original Articles: 2016 Vol: 8 Issue: 3

QSAR study of 5,6-bicyclic heterocycles analogues as anti-Alzheimer�¢����s agents using the statistical analysis methods

Abstract

Alzheimer’s disease (AD) is a chronic neurodegenerative disease. Current therapies of AD are only symptomatic, therefore the need for the development of new therapies to treat Alzheimer’s disease effectively. The 5,6-bicyclic heterocycles and its derivatives are potent anti-Alzheimer agents, these compounds inhibit β-amyloid (Aβ42). A study of quantitative structure-activity relationship (QSAR) is applied to a set of 34 molecules derived from 5,6-bicyclic heterocycles, in order to predict the anti-Alzheimer biological activity of the test compounds and find a correlation between the different physic-chemical parameters (descriptors) of these compounds and its biological activity, usingprincipal components analysis(PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and the artificial neural network (ANN). We accordingly propose a quantitative model (non-linear and linear QSAR models), and we interpret the activity of the compounds relying on the multivariate statistical analysis. The topological descriptors were computed, respectively, with ACD/ChemSketch and ChemBioDraw Ultra 14.0 programs. A good correlation was found between the experimental activity and those obtained by MLR and MNLR respectively such as (R = 0,843 and R2 = 0,712) and (R = 0,870 and R2 = 0,758), this result could be improved with ANN such as (R = 0,924 and R2 = 0,853) with an architecture ANN (7-2-1). To test the performance of the neural network and the validity of our choice of descriptors selected by MLR and trained by MNLR and ANN, we used cross-validation method (CV) such as (R = 0,874 and R2 = 0,763) with the procedure leave-one-out (LOO). This study show that the MLR and MNLR have served to predict activities, but when compared with the results given by an 7-2-1 ANN model we realized that the predictions fulfilled by this latter was more effective and much better than other models. The statistical results indicate that this model is statistically significant and shows very good stability towards data variation in leave-one-out (LOO) cross validation.