Molecule mining

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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.

Coding(Moleculei,Moleculeji)[edit]

Kernel methods[edit]

  • Marginalized graph kernel[1]
  • Optimal assignment kernel[2][3][4]
  • Pharmacophore kernel[5]
  • C++ (and R) implementation combining
    • the marginalized graph kernel between labeled graphs[1]
    • extensions of the marginalized kernel[6]
    • Tanimoto kernels[7]
    • graph kernels based on tree patterns[8]
    • kernels based on pharmacophores for 3D structure of molecules[5]

Maximum Common Graph methods[edit]

  • MCS-HSCS[9] (Highest Scoring Common Substructure (HSCS) ranking strategy for single MCS)
  • Small Molecule Subgraph Detector (SMSD)[10]- is a Java-based software library for calculating Maximum Common Subgraph (MCS) between small molecules. This will help us to find similarity/distance between two molecules. MCS is also used for screening drug like compounds by hitting molecules, which share common subgraph (substructure).[11]

Coding(Moleculei)[edit]

Molecular query methods[edit]

Methods based on special architectures of neural networks[edit]

See also[edit]

References[edit]

  1. ^ a b H. Kashima, K. Tsuda, A. Inokuchi, Marginalized Kernels Between Labeled Graphs, The 20th International Conference on Machine Learning (ICML2003), 2003. PDF
  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
  5. ^ a b P. Mahe, L. Ralaivola, V. Stoven, J. Vert, The pharmacophore kernel for virtual screening with support vector machines, J Chem Inf Model, 2006, 46, 2003-2014. doi:10.1021/ci060138m
  6. ^ P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret and P. Vert, J.-P. (2004). "Extensions of marginalized graph kernels". Proceedings of the 21st ICML: 552–559.  horizontal tab character in |author= at position 56 (help)
  7. ^ L. Ralaivola, S. J. Swamidass, S. Hiroto and P. Baldi (2005). "Graph kernels for chemical informatics". Neural Networks. 18: 1093–1110. doi:10.1016/j.neunet.2005.07.009. 
  8. ^ P. Mahé and J.-P. Vert (2009). "Graph kernels based on tree patterns for molecules". Machine Learning. 75 (1): 3–35. doi:10.1007/s10994-008-5086-2. ISSN 0885-6125. 
  9. ^ 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
  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. ^ http://www.ebi.ac.uk/thornton-srv/software/SMSD/
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  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.
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  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.
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Further reading[edit]

  • 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

See also[edit]

External links[edit]