Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Mining of Chemical Data

  • Xifeng YanEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1299


Given a set of chemical compounds, chemical data mining is to characterize the compounds present in the data set and apply a variety of mining methods to discover relationships between the compounds and their biological and chemical activities.

Historical Background

In 1969, Hansch [6] introduced quantitative structure-activity relationship (QSAR) analysis which attempts to correlate physicochemical or structural properties of compounds with biological and chemical activities. These physicochemical and structural properties are determined empirically or by computational methods. QSAR prefers vectorial mappings of compounds, which are usually coded by existing physicochemical and structural fingerprints. Dehaspe et al. [3] applied inductive logic programming to predict chemical carcinogenicity by mining frequent substructures in chemical datasets, which identifies new structural fingerprints so that QSAR could build comprehensive analytical models.


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Recommended Reading

  1. 1.
    Bunke H, Shearer K. A graph distance metric based on the maximal common subgraph. Pattern Recogn Lett. 1998;19(3):255–9.zbMATHCrossRefGoogle Scholar
  2. 2.
    Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  3. 3.
    Dehaspe L, Toivonen H, King R. Finding frequent substructures in chemical compounds. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining; 1998. p. 30–6.Google Scholar
  4. 4.
    Deshpande M, Kuramochi M, Wale N, Karypis G. Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans Knowl Data Eng. 2005;17(8):1036–50.CrossRefGoogle Scholar
  5. 5.
    Fröhlich H, Wegner J, Sieker F, Zell A. Optimal assignment kernels for attributed molecular graphs. In: Proceedings of the 22nd International Conference on Machine Learning; 2005. p. 225–32.Google Scholar
  6. 6.
    Hansch C. A quantitative approach to biochemical structure-activity relationships. Acc Chem Res. 1969;2(8):232–9.CrossRefGoogle Scholar
  7. 7.
    Kashima H, Tsuda K, Inokuchi A. Marginalized kernels between labeled graphs. In: Proceedings of the 20th International Conference on Machine Learning; 2003. p. 321–28.Google Scholar
  8. 8.
    Kramer S, Raedt L, Helma C. Molecular feature mining in HIV data. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001. p. 136–43.Google Scholar
  9. 9.
    Yan X, Yu PS, Han J. Graph indexing: a frequent structure-based approach. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2004. p. 335–46.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBM T. J. Watson Research CenterHawthorneUSA

Section editors and affiliations

  • Amarnath Gupta
    • 1
  1. 1.San Diego Supercomputer CenterUniv. of California San DiegoLa JollaUSA