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Molecule mining



This page describes mining for molecules. Since molecules may be represented by molecular graphs this is strongly related to graph mining and structured data mining. The main problem is how to represent molecules while discriminating the data instances. One way to do this is chemical similarity metrics, which has a long tradition in the field of cheminformatics.

Typical approaches to calculate chemical similarities use chemical fingerprints, but this loses the underlying information about the molecule topology. Mining the molecular graphs directly avoids this problem. So does the inverse QSAR problem which is preferable for vectorial mappings.

Additional recommended knowledge

Contents

Coding(Moleculei,Moleculej\neqi)

Kernel methods

  • Marginalized graph kernel[1]
  • Optimal assignment kernel[2][3][4]
  • Pharmacophore kernel[5]

Maximum Common Graph methods

  • MCS-HSCS[6] (Highest Scoring Common Substructure (HSCS) ranking strategy for single MCS)

Coding(Moleculei)

Molecular query methods

See also

References

  • 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 3527299130
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  2. ^ H. Fröhlich, J. K. Wegner, A. Zell, Optimal Assignment Kernels For Attributed Molecular Graphs, The 22nd International Conference on Machine Learning (ICML 2005), Omnipress, Madison, WI, USA, 2005, 225-232. PDF
  3. ^ H. Fröhlich, J. K. Wegner, A. Zell, Kernel Functions for Attributed Molecular Graphs - A New Similarity Based Approach To ADME Prediction in Classification and Regression, QSAR Comb. Sci., 2006, 25, 317-326. DOI 10.1002/qsar.200510135
  4. ^ H. Fröhlich, J. K. Wegner, A. Zell, Assignment Kernels For Chemical Compounds, International Joint Conference on Neural Networks 2005 (IJCNN'05), 2005, 913-918. CiteSeer
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  6. ^ J. K. Wegner, H. Fröhlich, H. Mielenz, A. Zell, Data and Graph Mining in Chemical Space for ADME and Activity Data Sets, QSAR Comb. Sci., 2006, 25, 205-220. DOI 10.1002/qsar.200510009
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  9. ^ T. Meinl, M. R. Berthold, Hybrid Fragment Mining with MoFa and FSG, Proceedings of the 2004 IEEE Conference on Systems, Man & Cybernetics (SMC2004), 2004.
  10. ^ M. Wörlein, Extension and parallelization of a graph-mining-algorithm, Friedrich-Alexander-Universität, 2006. PDF
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  14. ^ 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.
  15. ^ 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.
  16. ^ A. Inokuchi, T. Washio, K. Nishimura, H. Motoda, A Fast Algorithm for Mining Frequent Connected Subgraphs, IBM Research, Tokyo Research Laboratory, 2002.
  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. ^ 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
  20. ^ 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.
  21. ^ K. Jahn, S. Kramer, Optimizing gSpan for Molecular Datasets, Proceedings of the Third International Workshop on Mining Graphs, Trees and Sequences (MGTS-2005), 2005.
  22. ^ 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.
  23. ^ 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
  24. ^ 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
  25. ^ C. Helma, Predictive Toxicology, CRC Press, 2005.

See also

 
This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Molecule_mining". A list of authors is available in Wikipedia.
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