三维距离矩阵及预测烷烃的临界温度  

Three-dimensional distance matrix and prediction of the critical temperatures of alkanes

作　　者：葛明兰[1] 邓文生[1] 王利生[1] 秦莹[1] 

机构地区：[1]北京理工大学化工与环境学院

出　　处：《计算机与应用化学》2008年第1期123-125,共3页Computers and Applied Chemistry

摘　　要：本文从有机化合物分子三维空间结构出发,用其原子间的空间距离以替代二维距离矩阵,传统表示方法中的拓扑距离(键的数目),首次提出并建立表示分子空间结构的三维距离矩阵计算模型,并基于三维距离矩阵提出三维结构拓扑指数3- DM(W),将分子所含原子个数和支化度组合作为BP人工神经网络(BPANN)的输入参数,利用BPANN预测烷烃化合物的临界温度(T_c),结果精度符合化工计算的要求。可以预料三维距离矩阵计算模型极可能在定量结构-性能关系研究中起重要作用。Base on the introduction of the space distance between atoms of the compound moleculars instead of the traditional topographic distance in two-dimensional topographic distance matrix, a model of three-Dimensional distance matrix which reflected the space structures of the compound moleculars was developed. A novel three-dimensional topological index 3-DM （W） was proposed on the basis of this three-dimensional distance matrix and the same calculation formular of Wiener index （W）. A back-propagation artificial neural networks （BPANN） model was used to predict the critical temperatures of alkanes with 3-DM （ W）, number of atom and degree of branching as the input parameters. The predicted results showed a better agreement with the experimental values. It can be seen that the three-dimensional distance matrix may play an important role in quantitative structure-property relationship （QSPR） studies.

关 键 词：三维距离矩阵 拓扑指数 BP人工神经网络 烷烃 临界温度 

分 类 号：O65[理学—分析化学]