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

• Warmr^[12][13]
• AGM^[14][15]
• PolyFARM^[16]
• FSG^[17][18]
• MolFea^[19]
• MoFa/MoSS^[20][21][22]
• Gaston^[23]
• LAZAR^[24]
• ParMol^[25] (包括 MoFa, FFSM, gSpan 和 Gaston)
• optimized gSpan^[26][27]
• SMIREP^[28]
• DMax^[29]
• SAm/AIm/RHC^[30]
• AFGen^[31]
• gRed^[32]
• G-Hash^[33]

基于神经网络特殊架构的方法

• BPZ^[34][35]
• ChemNet^[36]
• CCS^[37][38]
• MolNet^[39]
• Graph machines^[40]

参见

• 分子查询语言
• 化学图论
  

参考文献

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6. P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret and P. Vert, J.-P. Extensions of marginalized graph kernels. Proceedings of the 21st ICML. 2004: 552–559.
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10. S. A. Rahman, M. Bashton, G. L. Holliday, R. Schrader and J. M. Thornton, Small Molecule Subgraph Detector (SMSD) toolkit, Journal of Cheminformatics 2009, 1:12. doi:10.1186/1758-2946-1-12
11. 存档副本. [2017-07-02]. （原始内容存档于2020-01-28）.
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13. L. Dehaspe, H. Toivonen, King, Finding frequent substructures in chemical compounds, 4th International Conference on Knowledge Discovery and Data Mining, AAAI Press., 1998, 30-36.
14. A. Inokuchi, T. Washio, T. Okada, H. Motoda, Applying the Apriori-based Graph Mining Method to Mutagenesis Data Analysis, Journal of Computer Aided Chemistry, 2001, 2, 87-92.
15. A. Inokuchi, T. Washio, K. Nishimura, H. Motoda, A Fast Algorithm for Mining Frequent Connected Subgraphs, IBM Research, Tokyo Research Laboratory, 2002.
16. A. Clare, R. D. King, Data mining the yeast genome in a lazy functional language, Practical Aspects of Declarative Languages (PADL2003), 2003.
17. M. Kuramochi, G. Karypis, An Efficient Algorithm for Discovering Frequent Subgraphs, IEEE Transactions on Knowledge and Data Engineering, 2004, 16(9), 1038-1051.
18. M. Deshpande, M. Kuramochi, N. Wale, G. Karypis, Frequent Substructure-Based Approaches for Classifying Chemical Compounds, IEEE Transactions on Knowledge and Data Engineering, 2005, 17(8), 1036-1050.
19. C. Helma, T. Cramer, S. Kramer, L. de Raedt, Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds, J. Chem. Inf. Comput. Sci., 2004, 44, 1402-1411. doi:10.1021/ci034254q
20. T. Meinl, C. Borgelt, M. R. Berthold, Discriminative Closed Fragment Mining and Perfect Extensions in MoFa, Proceedings of the Second Starting AI Researchers Symposium (STAIRS 2004), 2004.
21. T. Meinl, C. Borgelt, M. R. Berthold, M. Philippsen, Mining Fragments with Fuzzy Chains in Molecular Databases, Second International Workshop on Mining Graphs, Trees and Sequences (MGTS2004), 2004.
22. T. Meinl, M. R. Berthold, Hybrid Fragment Mining with MoFa and FSG, Proceedings of the 2004 IEEE Conference on Systems, Man & Cybernetics (SMC2004), 2004.
23. S. Nijssen, J. N. Kok. Frequent Graph Mining and its Application to Molecular Databases, Proceedings of the 2004 IEEE Conference on Systems, Man & Cybernetics (SMC2004), 2004.
24. C. Helma, Predictive Toxicology, CRC Press, 2005.
25. M. Wörlein, Extension and parallelization of a graph-mining-algorithm, Friedrich-Alexander-Universität, 2006. PDF
26. K. Jahn, S. Kramer, Optimizing gSpan for Molecular Datasets, Proceedings of the Third International Workshop on Mining Graphs, Trees and Sequences (MGTS-2005), 2005.
27. X. Yan, J. Han, gSpan: Graph-Based Substructure Pattern Mining, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), IEEE Computer Society, 2002, 721-724.
28. A. Karwath, L. D. Raedt, SMIREP: predicting chemical activity from SMILES, J Chem Inf Model, 2006, 46, 2432-2444. doi:10.1021/ci060159g
29. H. Ando, L. Dehaspe, W. Luyten, E. Craenenbroeck, H. Vandecasteele, L. Meervelt, Discovering H-Bonding Rules in Crystals with Inductive Logic Programming, Mol Pharm, 2006, 3, 665-674 . doi:10.1021/mp060034z
30. P. Mazzatorta, L. Tran, B. Schilter, M. Grigorov, Integration of Structure-Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity, J. Chem. Inf. Model., 2006, ASAP alert. doi:10.1021/ci600411v
31. N. Wale, G. Karypis. Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification, ICDM, ''2006, 678-689.
32. A. Gago Alonso, J.E. Medina Pagola, J.A. Carrasco-Ochoa and J.F. Martínez-Trinidad Mining Connected Subgraph Mining Reducing the Number of Candidates, In Proc. of ECML--PKDD, pp. 365–376, 2008.
33. Xiaohong Wang, Jun Huan , Aaron Smalter, Gerald Lushington, Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases , in BMC Bioinformatics Vol. 11 (Suppl 3):S8 2010.
34. Baskin, I. I.; V. A. Palyulin; N. S. Zefirov. [A methodology for searching direct correlations between structures and properties of organic compounds by using computational neural networks]. Doklady Akademii Nauk SSSR. 1993, 333 (2): 176–179.
35. I. I. Baskin, V. A. Palyulin, N. S. Zefirov. A Neural Device for Searching Direct Correlations between Structures and Properties of Organic Compounds. J. Chem. Inf. Comput. Sci. 1997, 37 (4): 715–721. doi:10.1021/ci940128y.
36. D. B. Kireev. ChemNet: A Novel Neural Network Based Method for Graph/Property Mapping. J. Chem. Inf. Comput. Sci. 1995, 35 (2): 175–180. doi:10.1021/ci00024a001.
37. A. M. Bianucci; Micheli, Alessio; Sperduti, Alessandro; Starita, Antonina. Application of Cascade Correlation Networks for Structures to Chemistry. Applied Intelligence. 2000, 12 (1-2): 117–146. doi:10.1023/A:1008368105614.
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40. A. Goulon, T. Picot, A. Duprat, G. Dreyfus. Predicting activities without computing descriptors: Graph machines for QSAR. SAR and QSAR in Environmental Research. 2007, 18 (1-2): 141–153. PMID 17365965. doi:10.1080/10629360601054313.

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

• 定量构效关系
• ADME
• 分配系数

外部链接

• 小分子子图检测器(SMSD) （页面存档备份，存于互联网档案馆） - 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）。
• 2007年第五届国际挖掘与学习研讨会 （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）
• 分子开采（基础化学专家系统）
• ParMol 和 硕士论文文档（页面存档备份，存于互联网档案馆） - Java - 开源 - 分布式挖掘 - 基准算法库
• TU慕尼黑 - 克莱默集团
• 分子采矿（高级化学专家系统）
• DMax化学助理 -商业软件
• AFGen （页面存档备份，存于互联网档案馆） -用于生成基于片段的描述符的软件