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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

杜雨静1,2,范英芳1   

  1. 1山西大学分子科学研究所

    2长治职业技术学院基础部

  • 出版日期:2010-01-05 发布日期:2010-01-05

QSAR study of triphenylacrylonitrile derivatives using artificial neural network

DU Yujing1,2,FAN Yingfang1   

  1. 1 Institute of Molecular Science

    2 Department of Basic Changzhi Vocational and Technical College

  • Online:2010-01-05 Published:2010-01-05

摘要: 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。

Abstract: 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

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