分子挖掘

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。 邊緣化圖形核心 最優分配核心 藥效核心 C++(and R)执行 （页面存档备份，存于互联网档案馆）结合 標記圖之間的邊緣化圖形核心 邊緣化核心的擴展 谷本核(Tanimoto kernels) 基於樹形圖的圖形內核 基於用於分子3D結構的藥效核心 MCS-HSCS (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）-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）。 Warmr AGM PolyFARM FSG MolFea MoFa/MoSS Gaston LAZAR ParMol (包括 MoFa, FFSM, gSpan 和 Gaston) optimized gSpan SMIREP DMax SAm/AIm/RHC AFGen gRed G-Hash BPZ ChemNet CCS MolNet Graph machines 分子查询语言 化學圖論 H. Kashima, K. Tsuda, A. Inokuchi, Marginalized Kernels Between Labeled Graphs, The 20th International Conference on Machine Learning (ICML2003), 2003. PDF H. Fröhlich, J. K. Wegner, A. 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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 定量构效关系 ADME 分配系数 小分子子图檢測器(SMSD) （页面存档备份，存于互联网档案馆） - 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）。 2007年第五屆國際挖掘與學習研討會 （页面存档备份，存于互联网档案馆） 2006年概览 （页面存档备份，存于互联网档案馆） 分子開採（基礎化學專家系統） ParMol 和 碩士論文文檔（页面存档备份，存于互联网档案馆） - Java - 開源 - 分佈式挖掘 - 基準算法庫 TU慕尼黑 - 克萊默集團 分子採礦（高級化學專家系統） DMax化學助理 -商业软件 AFGen （页面存档备份，存于互联网档案馆） -用於生成基於片段的描述符的軟件