Molecule mining

{{Cleanup|reason=The article is just lists. The reason for each list needs an introduction |date=September 2024}}

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)

=Kernel methods=

Marginalized graph kernelH. Kashima, K. Tsuda, A. Inokuchi, Marginalized Kernels Between Labeled Graphs, The 20th International Conference on Machine Learning (ICML2003), 2003. PDF
Optimal assignment kernelH. 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{{cite journal | author = Fröhlich H., Wegner J. K., Zell A. | year = 2006 | title = Kernel Functions for Attributed Molecular Graphs - A New Similarity Based Approach To ADME Prediction in Classification and Regression | journal = QSAR Comb. Sci. | volume = 25 | issue = 4 | pages = 317–326 | doi = 10.1002/qsar.200510135 }}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
Pharmacophore kernel{{cite journal | author = Mahe P., Ralaivola L., Stoven V., Vert J. | year = 2006 | title = The pharmacophore kernel for virtual screening with support vector machines | journal = J Chem Inf Model | volume = 46 | issue = 5 | pages = 2003–2014 | doi = 10.1021/ci060138m | pmid = 16995731 | arxiv = q-bio/0603006 | bibcode = 2006q.bio.....3006M | s2cid = 15060229 }}
[http://www.bioinf.jku.at/software/Rchemcpp/ C++ (and R) implementation] combining
the marginalized graph kernel between labeled graphs
extensions of the marginalized kernel{{cite journal |

author = P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret and P. Vert, J.-P. | title = Extensions of marginalized graph kernels | journal = Proceedings of the 21st ICML | year = 2004 | pages = 552–559 }}

Tanimoto kernels{{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 | issue =8 | pages = 1093–1110 | doi=10.1016/j.neunet.2005.07.009| pmid =16157471 }}
graph kernels based on tree patterns{{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 | doi = 10.1007/s10994-008-5086-2 |arxiv = q-bio/0609024 | s2cid = 5943581 }}
kernels based on pharmacophores for 3D structure of molecules

=Maximum common graph methods=

MCS-HSCS{{cite journal | author = Wegner J. K., Fröhlich H., Mielenz H., Zell A. | year = 2006 | title = Data and Graph Mining in Chemical Space for ADME and Activity Data Sets | journal = QSAR Comb. Sci. | volume = 25 | issue = 3 | pages = 205–220 | doi = 10.1002/qsar.200510009 }} (Highest Scoring Common Substructure (HSCS) ranking strategy for single MCS)
Small Molecule Subgraph Detector (SMSD){{cite journal | author = Rahman S. A., Bashton M., Holliday G. L., Schrader R., Thornton J. M. | year = 2009 | title = Small Molecule Subgraph Detector (SMSD) toolkit | journal = Journal of Cheminformatics | volume = 1 | issue = 1 | page = 12 | doi = 10.1186/1758-2946-1-12 | pmid = 20298518 | pmc = 2820491 | doi-access = free }}- 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).{{Cite web|url=http://www.ebi.ac.uk/thornton-srv/software/SMSD/|title=Small Molecule Subgraph Detector (SMSD)}}

Coding(Moleculei)

=Molecular query methods=

Warmr{{cite journal | author = King R. D., Srinivasan A., Dehaspe L. | year = 2001 | title = Wamr: a data mining tool for chemical data | journal = J. Comput.-Aid. Mol. Des. | volume = 15 | issue = 2 | pages = 173–181 | doi = 10.1023/A:1008171016861 | pmid = 11272703 | bibcode = 2001JCAMD..15..173K | s2cid = 3055046 }}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.
AGMA. 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.A. Inokuchi, T. Washio, K. Nishimura, H. Motoda, A Fast Algorithm for Mining Frequent Connected Subgraphs, IBM Research, Tokyo Research Laboratory, 2002.
PolyFARMA. Clare, R. D. King, Data mining the yeast genome in a lazy functional language, Practical Aspects of Declarative Languages (PADL2003), 2003.
FSG{{cite journal | author = Kuramochi M., Karypis G. | year = 2004 | title = An Efficient Algorithm for Discovering Frequent Subgraphs | journal = IEEE Transactions on Knowledge and Data Engineering | volume = 16 | issue = 9| pages = 1038–1051 | doi = 10.1109/tkde.2004.33 | s2cid = 242887 | citeseerx = 10.1.1.107.3913 }}{{cite journal | author = Deshpande M., Kuramochi M., Wale N., Karypis G. | year = 2005 | title = Frequent Substructure-Based Approaches for Classifying Chemical Compounds | journal = IEEE Transactions on Knowledge and Data Engineering | volume = 17 | issue = 8| pages = 1036–1050 | doi = 10.1109/tkde.2005.127 | hdl = 11299/215559 | hdl-access = free }}
MolFea{{cite journal | author = Helma C., Cramer T., Kramer S., de Raedt L. | year = 2004 | title = Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds | journal = J. Chem. Inf. Comput. Sci. | volume = 44 | issue = 4 | pages = 1402–1411 | doi = 10.1021/ci034254q | pmid = 15272848 }}
MoFa/MoSST. 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.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.{{cite book|first1 = T.|last1 = Meinl|first2= M. R. |last2= Berthold| title=2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583) | chapter=Hybrid fragment mining with MoFa and FSG |year=2004|volume = 5|pages = 4559–4564|doi = 10.1109/ICSMC.2004.1401250|isbn = 0-7803-8567-5|s2cid = 3248671| url=http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-67754 |chapter-url = http://www.uni-konstanz.de/bioml/bioml2/publications/Papers2004/MeBe04_mofafsg_smc.pdf}}
GastonS. 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.
LAZARC. Helma, Predictive Toxicology, CRC Press, 2005.
ParMolM. Wörlein, Extension and parallelization of a graph-mining-algorithm, Friedrich-Alexander-Universität, 2006. PDF (contains MoFa, FFSM, gSpan, and Gaston)
optimized gSpanK. Jahn, S. Kramer, Optimizing gSpan for Molecular Datasets, Proceedings of the Third International Workshop on Mining Graphs, Trees and Sequences (MGTS-2005), 2005.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.
SMIREP{{cite journal | author = Karwath A., Raedt L. D. | year = 2006 | title = SMIREP: predicting chemical activity from SMILES | journal = J Chem Inf Model | volume = 46 | issue = 6 | pages = 2432–2444 | doi = 10.1021/ci060159g | pmid = 17125185 | s2cid = 1460089 | url = https://lirias.kuleuven.be/handle/123456789/134667 }}
DMax{{cite journal | author = Ando H., Dehaspe L., Luyten W., Craenenbroeck E., Vandecasteele H., Meervelt L. | year = 2006 | title = Discovering H-Bonding Rules in Crystals with Inductive Logic Programming | journal = Mol Pharm | volume = 3 | issue = 6 | pages = 665–674 | doi = 10.1021/mp060034z | pmid = 17140254 | url = https://lirias.kuleuven.be/handle/123456789/20811 }}
SAm/AIm/RHC{{cite journal | author = Mazzatorta P., Tran L., Schilter B., Grigorov M. | year = 2007| title = Integration of Structure-Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity | journal = J. Chem. Inf. Model. | volume = 47| issue = 1| pages = 34–38| doi = 10.1021/ci600411v | pmid = 17238246}}
AFGen{{cite journal | author = Wale N., Karypis G. | title = Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification | journal = ICDM | volume = 2006 | pages = 678–689 }}
gRedA. Gago Alonso, J.E. Medina Pagola, J.A. Carrasco-Ochoa and J.F. Martínez-Trinidad Mining Connected Subgraph Mining Reducing the Number of Candidates, Proc. of ECML--PKDD, pp. 365–376, 2008.
G-HashXiaohong 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.

=Methods based on special architectures of neural networks=

BPZ{{cite journal | last = Baskin | first = I. I. |author2=V. A. Palyulin |author3=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}}{{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}}
ChemNet{{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}}
CCS{{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| s2cid = 10031212 }}{{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| citeseerx = 10.1.1.137.2895 }}
MolNet{{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}}
Graph machines{{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| bibcode = 2007SQER...18..141G | s2cid = 11759797 }}

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. {{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

[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.
[http://mlg07.dsi.unifi.it 5th International Workshop on Mining and Learning with Graphs, 2007]
[http://miningdrugs.blogspot.com/2007/01/molecule-mining-review-2006.html Overview for 2006]
[https://web.archive.org/web/20060429085450/http://hms.liacs.nl/molecules.html Molecule mining (basic chemical expert systems)]
[https://web.archive.org/web/20051125025336/http://www2.informatik.uni-erlangen.de/Forschung/Projekte/ParMol/?language=en ParMol] and [https://web.archive.org/web/20070610000211/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
[https://web.archive.org/web/20051120153619/http://wwwkramer.in.tum.de/ TU München - Kramer group]
[https://web.archive.org/web/20061217072112/http://joelib.sourceforge.net/wiki/index.php/Structured_Data_Mining Molecule mining (advanced chemical expert systems)]
[https://web.archive.org/web/20060828093721/http://www.pharmadm.com/DMaxChemistryAssistant.asp DMax Chemistry Assistant] - commercial software
[http://glaros.dtc.umn.edu/gkhome/afgen/overview AFGen] - Software for generating fragment-based descriptors

Category:Cheminformatics

Category:Computational chemistry

Category:Data mining