QSAR study of triphenylacrylonitrile derivatives using artificial neural network

The relationship between the affinity of 24 triphenylacrylonitrile derivatives acting on estrogen receptor in calf uterine tissue (lg1C) and X-hydroxy indicators (I),molecular surface area (SA), and the sum of net charge on B ring (QB) was discussed based on an improved back-propagation (BP) algorithm of artificial neural network (ANN). Selecting 20 compounds as the training set,the QSAR model was established with the ANN method. The residual 4 compounds as the prediction set were applied to test the predicted effect of the QSAR model. It was obtained that the correlation coefficient of QSAR model was R=0.9969 and the standard deviation was SD=0.0164. For the prediction set, R=0.9969 and SD=0.1533. The QSAR model for the same 24 compounds was also established with the multiple linear regression (MLR) method for comparison,with which R=0.9360 and SD=0.3779 were obtained. The results indicated that the fitted performance of ANN method is better than that of MLR model,which is comparatively precise and has a preferable predicted effect.