Molecule mining explained

Molecule mining is the process of data mining, or extracting and discovering patterns, as applied to 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 directlyavoids this problem. So does the inverse QSAR problem which is preferable for vectorial mappings.

Coding(Molecule_i,Molecule_j≠i)

Kernel methods

• 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

• 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(Molecule_i)

Molecular query methods

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

Methods based on special architectures of neural networks

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

See also

• Molecular Query Language
• Chemical graph theory
• Chemical space
• QSAR
• ADME
• partition coefficient

References

Further reading

• 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.
• Gusfield, D., Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Cambridge University Press, 1997.
• R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, 2000.

External links

• Small Molecule Subgraph Detector (SMSD) - is a Java-based software library for calculating Maximum Common Subgraph (MCS) between small molecules.
• 5th International Workshop on Mining and Learning with Graphs, 2007
• Overview for 2006
• Molecule mining (basic chemical expert systems)
• ParMol and master thesis documentation - Java - Open source - Distributed mining - Benchmark algorithm library
• TU München - Kramer group
• Molecule mining (advanced chemical expert systems)
• DMax Chemistry Assistant - commercial software
• AFGen - Software for generating fragment-based descriptors

Notes and References

1. 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. Fröhlich H., Wegner J. K., Zell A. . 2006 . Kernel Functions for Attributed Molecular Graphs - A New Similarity Based Approach To ADME Prediction in Classification and Regression . QSAR Comb. Sci. . 25 . 4 . 317–326 . 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. Mahe P., Ralaivola L., Stoven V., Vert J. . 2006 . The pharmacophore kernel for virtual screening with support vector machines . J Chem Inf Model . 46 . 5 . 2003–2014 . 10.1021/ci060138m . 16995731 . q-bio/0603006 . 2006q.bio.....3006M . 15060229 .
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 .
7. L. Ralaivola, S. J. Swamidass, S. Hiroto and P. Baldi. Graph kernels for chemical informatics . Neural Networks . 2005 . 18 . 8 . 1093–1110 . 10.1016/j.neunet.2005.07.009. 16157471 .
8. P. Mahé and J.-P. Vert. Graph kernels based on tree patterns for molecules . Machine Learning . 75 . 1 . 2009 . 0885-6125 . 3–35 . 10.1007/s10994-008-5086-2 . q-bio/0609024 . 5943581 .
9. Wegner J. K., Fröhlich H., Mielenz H., Zell A. . 2006 . Data and Graph Mining in Chemical Space for ADME and Activity Data Sets . QSAR Comb. Sci. . 25 . 3 . 205–220 . 10.1002/qsar.200510009 .
10. Rahman S. A., Bashton M., Holliday G. L., Schrader R., Thornton J. M. . 2009 . Small Molecule Subgraph Detector (SMSD) toolkit . Journal of Cheminformatics . 1 . 1 . 12 . 10.1186/1758-2946-1-12 . 20298518 . 2820491 . free .
11. Web site: Small Molecule Subgraph Detector (SMSD).
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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.
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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. Book: T.. Meinl. M. R. . Berthold. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583) . Hybrid fragment mining with MoFa and FSG . 2004. 5. 4559–4564. 10.1109/ICSMC.2004.1401250. 0-7803-8567-5. 3248671. http://www.uni-konstanz.de/bioml/bioml2/publications/Papers2004/MeBe04_mofafsg_smc.pdf.
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.
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31. Wale N., Karypis G. . Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification . ICDM . 2006 . 678–689 .
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33. Xiaohong Wang, Jun Huan, Aaron Smalter, Gerald Lushington, Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases , BMC Bioinformatics Vol. 11 (Suppl 3):S8 2010.
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