This page describes mining for molecules. Since molecules are multi-labeled 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.

See also

• QSAR
• ADME
• LogP

External links

• 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)

References

• C. Helma (ed.), Predictive Toxicology, CRC, 2005. ISBN 082472397X
• 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 0521585198
• R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, 2000. ISBN 3-52-29913-0

Cheminformatics