Nonlinear qsar study of xanthone and curcuminoid derivatives as α-glucosidase inhibitors

Abstract

A non linear QSAR model was constructed on a series of 57 xanthone and curcuminoide derivatives as α-glucosidase inhibitors by back-propagation neural network method. The neural network architecture was optimized to obtain a three-layer neural network, composed of five descriptors, nine hidden neurons and one output neuron. A good predictive determination coefficient was obtained (R2 Pset = 86.7%), the statistical results being better than those obtained with the same data set using a multiple regression analysis (MLR). As in the MLR model, the descriptor MATS7v weighted by Van der Waals volume was found as the most important independent variable on the α-glucosidase inhibitory.