含氮杂环化合物理化性质和生物活性的QSPR/QSAR分析

含氮杂环化合物理化性质和生物活性的QSPR/QSAR分析 QSPR/QSAR of Physicochemical Property and Bioactivity of Nitrogen-Containing Heterocyclic Compounds 陈艳 CHEN Yan 1 first-author 岳玮 YUE Wei 1 王彬 WANG Bin 1 徐州工程学院化学化工学院 徐州工程学院化学化工学院 School of Chemistry and Chemical Engineering,Xuzhou Institute of Technology School of Chemistry and Chemical Engineering,Xuzhou Institute of Technology 在分子拓扑理论的基础上,计算了含氮杂环化合物的3类拓扑指数:分子连接性指数、形状指数和分子电性距离矢量.利用多元线性回归的方法建立了含氮杂环化合物的疏水性参数（lgP）和对淡水纤毛虫毒性（-lgc）的QSPR/QSAR（定量结构-性质/活性相关）模型,相关系数分别为0.998 1和0.980 6.模型经Jackknife法和逐一剔除法（LOO）交互检验证明具有良好的稳健性和预测能力,为了进一步提高相关系数0.980 6模型的相关性,以模型中的3个参数为人工神经网络输入层,设定3∶3∶1的网络结构,构建人工神经网络（BP）算法模型,相关系数R提升为0.993 3.该模型能很好地预测含氮杂环化合物的理化性质和生物活性. Based on the chemical topological theory,Kier’s molecular connectivity indexes（Xi）,molecular shape indexes（Km）and molecular electronegativity-distance vector（Mk）were calculated.The QSPR/QSAR（quantitative structure-property/activity relationship）models of hydrophobic property parameter（lgP）and toxicity（-lgc）to freshwater ciliate of nitrogen-containing heterocyclic compounds were constructed by multiple linear regression with the correlation coefficient（R）of 0.998 1and 0.980 6,respectively.The models exhibited excellent stability and predictability evaluated by Jackknife method and leave-one-out（LOO）cross-validation procedure.In order to improve the correlation of the later model,three structural parameters were used as the input neurons of artificial neural network, and a 3∶3∶1network architecture was employed.A satisfied model could be constructed with the back-propagation algorithm,and the correlation coefficient R was 0.993 3.It is concluded that the models can be used to predict the physicochemical properties and bioactivities of nitrogen-containing heterocyclic compounds. 含氮杂环化合物 拓扑指数 疏水性参数 生物毒性 QSPR/QSAR（定量结构-性质/活性相关） nitrogen-containing heterocyclic compounds topological index hydrophobic property parameter toxicity QSPR/QSAR(quantitative structure-property/activity relationship 10.14188/j.1671-8836.2014.01.005 O626 国家自然科学基金（21272095）;徐州工程学院培育项目（XKY2011102）资助项目 2014-01-01 2021-04-01 1 60