高等学校化学学报

高等学校化学学报

• 研究论文 • 上一篇    下一篇

原子对空间距离法及其应用

张庆友1,2, 许禄2   

    1. 河南大学化学化工学院, 开封 475001;
    2. 中国科学院长春应用化学研究所, 长春 130022
  • 收稿日期:2007-07-27 修回日期:1900-01-01 出版日期:2008-07-10 发布日期:2008-07-10
  • 通讯作者: 许禄

Method of Atom-pair Space Distance and Its Application

ZHANG Qing-You1,2, XU Lu2*   

    1. College of Chemistry and Chemical Engineering, Henan University, Kaifeng 475001, China;
    2. Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
  • Received:2007-07-27 Revised:1900-01-01 Online:2008-07-10 Published:2008-07-10
  • Contact: XU Lu

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被引次数

1

摘要： 采用原子对空间距离法表征化合物的三维结构信息. 提出了计算端点的频数的方法, 与段的频数相比, 该方法能更好地描述原子对频数. 以不同段的距离, 分别采用上述两种频数计算得到化合物的相似度矩阵, 然后把相似度衍生为新的变量. 运用多元回归分析和人工神经网络分别构造了预测数学模型, 并对所得到的预测结果进行了比较. 这两种频数均较好地预测了HEPT类化合物的活性.

关键词: QSAR, 原子对空间距离法, 分子相似度, 多元回归分析, 人工神经网络

Abstract: In this article, atom-pairs of compounds, which include abundant three-dimensional information of molecules, were calculated. Vertex’s frequency of atom-pairs space distance was applied to describe the frequency of atom pairs, which is better than segment’s frequency of atom-pairs. Molecular similarity matrixes based on the two frequencies of atom-pairs in different distances of segments were calculated, respectively, and then these similarities were taken as the new variables. The mathematical models were built by using multiple regression analysis and artificial neural networks and the results were compared. The results of predictions of the activities of HEPT derivatives in both two frequencies are satisfactory.

Key words: QSAR, Atom-pairs space distance method, Molecular similarity, Multiple regression analysis, Artificial neural network

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