人工神经网络用于三苯基丙烯腈衍生物的定量结构-活性关系模型

采用人工神经网络(ANN)BP算法探讨了24个三苯基丙烯睛衍生物的lg1/C(C为半致死浓度)与X位羟基指示数I、分子表面积SA和B环上原子净电荷之和QB之间的关系,以20个样本为训练集建立了定量结构-活性关系(QSAR)模型,其相关系数和标准偏差分别为R=0.9969和SD=0.0164,其余4个样本为测试集,得到R=0.9913和SD=0.1533;用多元线性回归(MLR)方法建立的QSAR模型R=0.9360,SD=0.3779。结果表明,ANN方法具有良好的预测能力,比MLR方法更精密。

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 (1gl/C)and X-hydroxy indicators (I), molecular surface area (S_A), and the sum of net charge on B ring (Q_B) 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 deviationwas 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.