Molecule mining
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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.
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(Molecule i ,Molecule j≠i )
Kernel methods
Marginalized graph kernel Optimal assignment kernel Pharmacophore kernel C++ (and R) implementation combining the marginalized graph kernel between labeled graphs extensions of the marginalized kernel Tanimoto kernels graph kernels based on tree patterns kernels based on pharmacophores for 3D structure of molecules
Maximum common graph methods
MCS-HSCS (Highest Scoring Common Substructure (HSCS) ranking strategy for single MCS) Small Molecule Subgraph Detector (SMSD) - 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).
Coding(Molecule i )
Molecular query methods
Warmr AGM PolyFARM FSG MolFea MoFa/MoSS Gaston LAZAR ParMol (contains MoFa, FFSM, gSpan, and Gaston) optimized gSpan SMIREP DMax SAm/AIm/RHC AFGen gRed G-Hash
Methods based on special architectures of neural networks
BPZ ChemNet CCS MolNet Graph machines
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 Technical University of Munich (TUM) - Kramer group Molecule mining (advanced chemical expert systems) DMax Chemistry Assistant - commercial software AFGen - Software for generating fragment-based descriptors
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