Quantitative Structure-Activity Relationships (QSAR) Study of Flavonoid Derivatives for Inhibition of Cytochrome P450 1A2

Abstract

The quantitative structure-activity relationships (QSAR) studies on flavonoid derivatives as cytochrome P450 1A2 inhibitors were performed using multiple linear regression analysis (MLR) and neural networks (NN). The results of MLR and NN show that Hammett constant, the highest occupied molecular orbital energy (HOMO), the nonoverlap steric volume, the partial charge of C3 carbon atom, and the HOMO π coefficients of C3, C3′ and C4′ carbon atoms of flavonoids play an important role in inhibitory activity. The correlations between the descriptors and the activities were improved by neural networks although the descriptors of optimum MLR model were used in the networks, which implies that the descriptors used in MLR model include nonlinear relationships. Moreover, neural networks using descriptors selected by the pruning method gave higher r2 value than neural networks using MLR descriptors.