Combining DFT and QSAR computation to predict the interaction of flavonoids with the GABA (A) receptor using electronic and topological descriptors. Issue 3 (1st May 2017)

• Record Type: Journal Article
• Authors: Ghamali, M.; Chtita, S.; Aouidate, A.; Ghaleb, A.; Bouachrine, M.; Lakhlifi, T.
• Is Part Of: Journal of Taibah University for science. Volume 11:Issue 3(2017)
• Page Start: 422
• Page End: 433
• Publication Date: 2017-05-01
• Subjects: QSAR model -- DFT study -- Flavonoid derivatives -- GABA (A) receptor -- Artificial neural network (ANN); Science -- Periodicals; Science; Periodicals; 505
• DOI: 10.1016/j.jtusci.2016.06.005
• Languages: English
• ISSNs: 1658-3655

Abstract

Abstract: To establish a quantitative structure-activity relationship model of the binding affinity constants (−log K i ) of 41 flavonoid derivatives towards the GABA (A) receptor, the DFT-B3LYP method with basis set 6-31G (d) was performed to gain insights into the chemical structure and property information for the studied compounds. The best topological and electronic descriptors were selected. This work was conducted with principal component analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN). According to these analyses, we propose quantitative models and interpret the activity of the compounds based on multivariate statistical analysis. The statistical results of the MLR, MNLR and ANN indicate that the determination coefficients R 2 were 0.896, 0.925 and 0.916, respectively. The results show that the three modelling methods can predict the studied activity well and may be useful for predicting the biological activity of new compounds. The statistical results indicate that the models are statistically significant and stable with data variation in the external validation.