Molecule mining

Data mining for patterns in molecule data

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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 directly avoids this problem. So does the inverse QSAR problem which is preferable for vectorial mappings.

## Coding(Moleculei,Moleculej≠i)

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### Kernel methods

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* 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

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* 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)

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### Molecular query methods

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* 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

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

## See also

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

## References

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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. ^ 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\. doi:10.1002/qsar.200510135.`{{cite journal}}`: CS1 maint: multiple names: authors list (link)
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 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\. arXiv:q-bio/0603006. Bibcode:2006q.bio.....3006M. doi:10.1021/ci060138m. PMID 16995731. S2CID 15060229.`{{cite journal}}`: CS1 maint: multiple names: authors list (link)
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.`{{cite journal}}`: CS1 maint: multiple names: authors list (link)
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8. ^ P. Mahé and J.-P. Vert (2009). "Graph kernels based on tree patterns for molecules". Machine Learning. 75 (1): 3–35\. arXiv:q-bio/0609024. doi:10.1007/s10994-008-5086-2. ISSN 0885-6125. S2CID 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\. doi:10.1002/qsar.200510009.`{{cite journal}}`: CS1 maint: multiple names: authors list (link)
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11. ^ "Small Molecule Subgraph Detector (SMSD)".
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16. ^ A. Clare, R. D. King, Data mining the yeast genome in a lazy functional language, Practical Aspects of Declarative Languages (PADL2003), 2003.
17. ^ Kuramochi M., Karypis G. (2004). "An Efficient Algorithm for Discovering Frequent Subgraphs". IEEE Transactions on Knowledge and Data Engineering. 16 (9): 1038–1051\. CiteSeerX 10.1.1.107.3913. doi:10.1109/tkde.2004.33. S2CID 242887.
18. ^ Deshpande M., Kuramochi M., Wale N., Karypis G. (2005). "Frequent Substructure-Based Approaches for Classifying Chemical Compounds". IEEE Transactions on Knowledge and Data Engineering. 17 (8): 1036–1050\. doi:10.1109/tkde.2005.127. hdl:11299/215559.`{{cite journal}}`: CS1 maint: multiple names: authors list (link)
19. ^ Helma C., Cramer T., Kramer S., de Raedt L. (2004). "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. 44 (4): 1402–1411\. doi:10.1021/ci034254q. PMID 15272848.`{{cite journal}}`: CS1 maint: multiple names: authors list (link)
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. ^ Meinl, T.; Berthold, M. R. (2004). "Hybrid fragment mining with MoFa and FSG" (PDF). 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583). Vol. 5\. pp. 4559–4564\. doi:10.1109/ICSMC.2004.1401250. ISBN 0-7803-8567-5. S2CID 3248671.
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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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### Further reading

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* 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

## External links

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* 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