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| This page describes '''[[Data mining|mining]] for [[molecule]]s'''. Since molecules may be represented by [[molecular graph]]s 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 [[Metric (mathematics)|metrics]], which has a long tradition in the field of [[cheminformatics]].
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| Typical approaches to calculate chemical similarities use chemical fingerprints, but this loses the underlying information about the [[topology (chemistry)|molecule topology]]. Mining the molecular graphs directly
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| avoids this problem. So does the [[inverse QSAR problem]] which is preferable for vectorial mappings.
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| ==Coding(Molecule<sub>i</sub>,Molecule<sub>j<math>\neq</math>i</sub>)==
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| ===Kernel methods===
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| * Marginalized [[graph kernel]]<ref name="kti03">H. Kashima, K. Tsuda, A. Inokuchi, Marginalized Kernels Between Labeled Graphs, The 20th International Conference on Machine Learning (ICML2003), 2003. PDF</ref>
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| * Optimal assignment kernel<ref name="fwz05b"> 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</ref><ref name="fwz06"> 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}}</ref><ref name="fwz05a"> 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</ref>
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| * Pharmacophore kernel<ref name="mrsv06">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}}</ref>
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| * [http://www.bioinf.jku.at/software/Rchemcpp/ C++ (and R) implementation] combining
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| ** the marginalized graph kernel between labeled graphs<ref name="kti03"/>
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| ** extensions of the marginalized kernel<ref name="Mahe2004">{{cite journal |
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| author = P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret and P. Vert,
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| J.-P. | title = Extensions of marginalized graph kernels | journal = Proceedings of the 21st ICML | year = 2004 | pages = 552–559 }}</ref>
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| ** Tanimoto kernels<ref name="Ralaivola2005">{{cite journal | author =L. Ralaivola, S. J. Swamidass, S. Hiroto and P. Baldi| title =Graph kernels for chemical informatics | journal = Neural Networks | year = 2005 | volume = 18 | pages = 1093–1110 | url = http://www.sciencedirect.com/science/article/pii/S0893608005001693 | doi=10.1016/j.neunet.2005.07.009}}</ref>
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| ** graph kernels based on tree patterns<ref name="Mahe2009">{{cite journal | author = P. Mahé and J.-P. Vert| title = Graph kernels based on tree patterns for molecules | journal = Machine Learning | volume = 75 | number = 1 | year = 2009 | issn = 0885-6125 |pages = 3–35 | url = http://dx.doi.org/10.1007/s10994-008-5086-2 | doi = 10.1007/s10994-008-5086-2 }}</ref>
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| ** kernels based on pharmacophores for 3D structure of molecules<ref name="mrsv06"/>
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| ===Maximum Common Graph methods===
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| * [[Maximum common subgraph isomorphism problem|MCS]]-HSCS<ref name="wfmz06"> 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}}</ref> (Highest Scoring Common Substructure (HSCS) ranking strategy for single MCS)
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| * [[Small Molecule]] Subgraph Detector [[Small Molecule Subgraph Detector|(SMSD)]]<ref name="SMSD09">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}}</ref>- 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).<ref name="SMSD">http://www.ebi.ac.uk/thornton-srv/software/SMSD/</ref>
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| ==Coding(Molecule<sub>i</sub>)==
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| ===Molecular query methods===
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| * Warmr<ref name="ksd01">R. D. King, A. Srinivasan, L. Dehaspe, ''Wamr: a data mining tool for chemical data'', J. Comput.-Aid. Mol. Des., '''2001''', 15, 173-181. {{doi|10.1023/A:1008171016861}}</ref><ref name="dtk98">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.</ref>
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| * AGM<ref name="iwom01">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.</ref><ref name="iwnm02">A. Inokuchi, T. Washio, K. Nishimura, H. Motoda, ''A Fast Algorithm for Mining Frequent Connected Subgraphs'', IBM Research, Tokyo Research Laboratory, '''2002'''.</ref>
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| * PolyFARM<ref name="ck03">A. Clare, R. D. King, ''Data mining the yeast genome in a lazy functional language'', Practical Aspects of Declarative Languages (PADL2003), '''2003'''.</ref>
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| * FSG<ref name="fsg04">M. Kuramochi, G. Karypis, ''An Efficient Algorithm for Discovering Frequent Subgraphs'', IEEE Transactions on Knowledge and Data Engineering, '''2004''', 16(9), 1038-1051.</ref><ref name="fsg05">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.</ref>
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| * MolFea<ref name="hckr04"> 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}}</ref>
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| * MoFa/MoSS<ref name="mbb04">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'''.</ref><ref name="mbbp04">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'''.</ref><ref name="mb04">T. Meinl, M. R. Berthold, ''Hybrid Fragment Mining with MoFa and FSG'', Proceedings of the 2004 IEEE Conference on Systems, Man & Cybernetics (SMC2004), '''2004'''.</ref>
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| * Gaston<ref name="nk04">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'''.</ref>
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| * LAZAR<ref name="hel05">C. Helma, Predictive Toxicology, CRC Press, '''2005'''.</ref>
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| * ParMol<ref name="woe06">M. Wörlein, ''Extension and parallelization of a graph-mining-algorithm'', Friedrich-Alexander-Universität, '''2006'''. PDF</ref> (contains MoFa, FFSM, gSpan, and Gaston)
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| * optimized gSpan<ref name="jk05">K. Jahn, S. Kramer, ''Optimizing gSpan for Molecular Datasets'', Proceedings of the Third International Workshop on Mining Graphs, Trees and Sequences (MGTS-2005), '''2005'''.</ref><ref name="yh02a">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.</ref>
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| * SMIREP<ref name="kr06">A. Karwath, L. D. Raedt, ''SMIREP: predicting chemical activity from SMILES'', J Chem Inf Model, '''2006''', 46, 2432-2444. {{doi|10.1021/ci060159g}}</ref>
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| * DMax<ref name="ahlcvm06">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}}</ref>
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| * SAm/AIm/RHC<ref name="mtsg06">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}}</ref>
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| * AFGen<ref name="afgen06">N. Wale, G. Karypis. ''Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification'', ICDM, ''''2006'', 678-689.</ref>
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| * gRed<ref name="gago08">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''.</ref>
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| * G-Hash<ref name="wang10">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''.</ref>
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| ===Methods based on special architectures of neural networks===
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| * BPZ<ref>{{cite journal | last = Baskin | first = I. I. | coauthors = V. A. Palyulin and N. S. Zefirov | title = [A methodology for searching direct correlations between structures and properties of organic compounds by using computational neural networks] | journal = [[Doklady Akademii Nauk SSSR]] | year = 1993 | volume = 333 | issue = 2 | pages = 176–179}}</ref><ref>{{cite journal | author = I. I. Baskin, V. A. Palyulin, N. S. Zefirov | title = A Neural Device for Searching Direct Correlations between Structures and Properties of Organic Compounds | journal = J. Chem. Inf. Comput. Sci. | year = 1997 | volume = 37 | issue = 4 | pages = 715–721 | doi = 10.1021/ci940128y}}</ref>
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| * ChemNet<ref>{{cite journal | author = D. B. Kireev | title = ChemNet: A Novel Neural Network Based Method for Graph/Property Mapping | journal = J. Chem. Inf. Comput. Sci. | year = 1995 | volume = 35 | issue = 2 | pages = 175–180 | doi = 10.1021/ci00024a001}}</ref>
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| * CCS<ref>{{cite journal | doi = 10.1023/A:1008368105614 | author = A. M. Bianucci | last2 = Micheli | first2 = Alessio | last3 = Sperduti | first3 = Alessandro | last4 = Starita | first4 = Antonina | title = Application of Cascade Correlation Networks for Structures to Chemistry | journal = Applied Intelligence | year = 2000 | volume = 12 | issue = 1-2 | pages = 117–146}}</ref><ref>{{cite journal | author = A. Micheli, A. Sperduti, A. Starita, A. M. Bianucci | title = Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines | journal = J. Chem. Inf. Comput. Sci. | year = 2001 | volume = 41 | issue = 1 | pages = 202–218 | doi = 10.1021/ci9903399 | pmid = 11206375}}</ref>
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| * MolNet<ref>{{cite journal | author = O. Ivanciuc | title = Molecular Structure Encoding into Artificial Neural Networks Topology | journal = Roumanian Chemical Quarterly Reviews | year = 2001 | volume = 8 | pages = 197–220}}</ref>
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| * Graph machines<ref>{{cite journal | author = A. Goulon, T. Picot, A. Duprat, G. Dreyfus | title = Predicting activities without computing descriptors: Graph machines for QSAR | journal = SAR and QSAR in Environmental Research | year = 2007 | volume = 18 | issue = 1-2 | pages = 141–153 | doi = 10.1080/10629360601054313 | pmid = 17365965}}</ref>
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| == See also ==
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| * [[Molecular Query Language]]
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| * [[Chemical graph theory]]
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| ==References==
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| {{reflist|2}}
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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'''.
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| * R.O. Duda, P.E. Hart, D.G. Stork, ''Pattern Classification'', John Wiley & Sons, '''2001'''. ISBN 0-471-05669-3
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| * Gusfield, D., ''Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology'', Cambridge University Press, '''1997'''. ISBN 0-521-58519-8
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| * R. Todeschini, V. Consonni, ''Handbook of Molecular Descriptors'', Wiley-VCH, '''2000'''. ISBN 3-527-29913-0
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| ==See also==
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| *[[QSAR]]
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| *[[ADME]]
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| *[[partition coefficient]]
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| ==External links==
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| * [http://www.ebi.ac.uk/thornton-srv/software/SMSD/ Small Molecule Subgraph Detector (SMSD)] - is a Java based software library for calculating Maximum Common Subgraph (MCS) between small molecules.
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| * [http://mlg07.dsi.unifi.it 5th International Workshop on Mining and Learning with Graphs, 2007]
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| * [http://miningdrugs.blogspot.com/2007/01/molecule-mining-review-2006.html Overview for 2006]
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| * [http://hms.liacs.nl/molecules.html Molecule mining (basic chemical expert systems)]
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| * [http://www2.informatik.uni-erlangen.de/Forschung/Projekte/ParMol/?language=en ParMol] and [http://www2.informatik.uni-erlangen.de/Lehre/SA-DA/download/DA-simawoer.pdf?language=en master thesis documentation] - Java - Open source - Distributed mining - Benchmark algorithm library
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| * [http://wwwkramer.in.tum.de/ TU München - Kramer group]
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| * [http://joelib.sourceforge.net/wiki/index.php/Structured_Data_Mining Molecule mining (advanced chemical expert systems)]
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| * [http://www.pharmadm.com/DMaxChemistryAssistant.asp DMax Chemistry Assistant] - commercial software
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| * [http://glaros.dtc.umn.edu/gkhome/afgen/overview AFGen] - Software for generating fragment-based descriptors
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| [[Category:Cheminformatics]]
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| [[Category:Computational chemistry]]
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| [[Category:Data mining]]
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