人工神经网络用于异噻唑啉酮的定量结构-活性关系研究OA北大核心CSCDCSTPCD
QSAR of 3-isothiazolinone compounds using artificial neural network
采用误差反传前向人工神经网络(artificial neural network,ANN)建立了21种2-(4-取代-苯基)-3-异噻唑啉酮类化合物的结构与其抗菌活性之间的定量关系模型(ANN模型),以21种3-异噻唑啉酮类化合物的量子化学参数和拓扑指数作为输入、抗菌活性作为输出,所构建网络模型的交叉检验相关系数为0.991 6、标准偏差为0.080 1、残差绝对值≤0.221,应用于外部预测集,预测集相关系数为0.973 1；而多元线性回归(m…查看全部>>
The study of the quantitative structure-activity relationship (QSAR) on 21 kinds of 2- (4- substi tuted-phenyl) -3-isothiazolinones was established by the artificial neural network based on the back propagation algorithm. For the artificial neural network method, the quantum chemical parameters about structure and the mo lecular topological index were used as the inputs of the neural network, and the antibacterial activities as the out puts of the neur…查看全部>>
何琴;黄保军;李公春
许昌学院化学化工学院,许昌461000许昌学院表面微纳米材料研究所,许昌461000许昌学院化学化工学院,许昌461000
化学化工
异噻唑啉酮定量结构-活性关系人工神经网络抗菌活性大肠杆菌
3-isothiazolinone quantitative structure-activity relationship artificial neural network antibacterial activity Escherichia coli
《安徽农业大学学报》 2012 (2)
323-326,4
河南省教育厅自然科学研究计划项目(2009B150023)资助.
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