分子挖掘

本页使用了标题或全文手工转换
维基百科，自由的百科全书

分子探勘（Molecule mining）為使用分子的数据挖掘。由於分子可由分子圖表示，這與圖形挖掘和結構化數據挖掘密切相關。主要問題是如何在區分數據實例時表示分子。其中一種方法是化學相似性度量，这在化學信息學領域具有悠久的傳統。

計算化學相似性的典型方法是使用化學指紋，但這会导致丟失有關分子拓撲的基礎信息。挖掘分子圖直接避免了這個問題。反向QSAR問題也適用於矢量映射問題。

編碼(分子i,分子j\neq i)[编辑]

核心方法[编辑]

最大值共同圖形方法(Maximum Common Graph methods)[编辑]

  • MCS-HSCS[9] (單MCS最高得分普通子結構（HSCS）排名策略)
  • 小分子子图檢測器（SMSD）[10]-是一個基於Java的軟件庫，用於計算小分子之間的最大共同子圖（MCS）。這將有助於我們找到兩個分子之間的相似性/距離。 MCS也用於通過擊打分子來篩選藥物化合物，其分享共同的子圖（子結構）。[11]

編碼（分子i）[编辑]

分子查詢方法[编辑]

基於神經網絡特殊架構的方法[编辑]

参见[编辑]

参考文献[编辑]

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进一步阅读[编辑]

  • 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[编辑]

外部链接[编辑]

取自“https://zh.wikipedia.org/w/index.php?title=分子挖掘&oldid=70628197”