分子挖掘

分子挖掘（Molecule mining）为使用分子的数据挖掘。由于分子可由分子图表示，这与图形挖掘和结构化数据挖掘密切相关。主要问题是如何在区分数据实例时表示分子。其中一种方法是化学相似性度量，这在化学信息学领域具有悠久的传统。

计算化学相似性的典型方法是使用化学指纹，但这会导致丢失有关分子拓扑的基础信息。挖掘分子图直接避免了这个问题。反向QSAR问题也适用于矢量映射问题。

编码(分子i,分子j\neq i)

核心方法

  • 边缘化图形核心
    [1]
  • 最优分配核心[2][3][4]
  • 药效核心[5]
  • C++(and R)执行 （页面存档备份，存于互联网档案馆）结合
    • 标记图之间的边缘化图形核心
      [1]
    • 边缘化核心的扩展[6]
    • 谷本核(Tanimoto kernels)[7]
    • 基于树形图的图形内核[8]
    • 基于用于分子3D结构的药效核心[5]

最大值共同图形方法(Maximum Common Graph methods)

  • MCS-HSCS[9] (单MCS最高得分普通子结构（HSCS）排名策略)
  • 小分子子图检测器（SMSD）[10]-是一个基于Java的软件库，用于计算小分子之间的最大共同子图（MCS）。这将有助于我们找到两个分子之间的相似性/距离。 MCS也用于通过击打分子来筛选药物化合物，其分享共同的子图（子结构）。[11]

编码（分子i）

分子查询方法

基于神经网络特殊架构的方法

参见

参考文献

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进一步阅读

  • Schölkopf, B., K. Tsuda and J. P. Vert: Kernel Methods in Computational Biology, MIT Press, Cambridge, MA, 2004.
  • R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, John Wiley & Sons, 2001. ISBN 0-471-05669-3
  • Gusfield, D., Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Cambridge University Press, 1997。 ISBN 0-521-58519-8
  • R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, 2000. ISBN 3-527-29913-0

参见

外部链接