Combining DFT and QSAR computation to predict the interaction of flavonoids with the GABA (A) receptor using electronic and topological descriptors

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.

QSAR Study of (5-Nitroheteroaryl-1,3,4-thiadiazole-2- yl)piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

“…Using these new variables, the dimensionality of the system is reduced with a minimum loss of information 15 . The obtained matrix of coordinates allows us to analyze the dispersion of individuals in the new defined space [16][17][18] . After that, the principal component analysis (PCA) was used to determine the non-linearity and nonmulticollinearity among variables and to select descriptors that correlate with the activity.…”

QSAR Study of (5-Nitroheteroaryl-1,3,4-Thiadiazole-2-yl) Piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

“…Using these new variables, the dimensionality of the system is reduced with a minimum loss of information [15]. The obtained matrix of coordinates allows us to analyze the dispersion of individuals in the new defined space [16][17][18]. After that, the principal component analysis (PCA) was used to determine the nonlinearity and non-multicollinearity among variables and to select descriptors that correlate with the activity.…”