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Managing and Mining Graph Data pp 581–606Cite as

Trends in Chemical Graph Data Mining

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Part of the book series: Advances in Database Systems ((ADBS,volume 40))

Abstract

Mining chemical compounds in silico has drawn increasing attention from both academia and pharmaceutical industry due to its effectiveness in aiding the drug discovery process. Since graphs are the natural representation for chemical compounds, most of the mining algorithms focus on mining chemical graphs. Chemical graph mining approaches have many applications in the drug discovery process that include structure-activity-relationship (SAR) model construction and bioactivity classification, similar compound search and retrieval from chemical compound database, target identification from phenotypic assays, etc. Solving such problems in silico through studying and mining chemical graphs can provide novel perspective to medicinal chemists, biologist and toxicologist. Moreover, since the large scale chemical graph mining is usually employed at the early stages of drug discovery, it has the potential to speed up the entire drug discovery process. In this chapter, we discuss various problems and algorithms related to mining chemical graphs and describe some of the state-of-the-art chemical graph mining methodologies and their applications.

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Author information

Authors and Affiliations

  1. Computer Science & Engineering University of Minnesota, Twin Cities, US

    Nikil Wale, Xia Ning & George Karypis

Authors
  1. Nikil Wale
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  2. Xia Ning
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  3. George Karypis
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Correspondence to Nikil Wale .

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Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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Wale, N., Ning, X., Karypis, G. (2010). Trends in Chemical Graph Data Mining. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_19

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_19

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