bookchapter.bib

@incollection{Wale2010,
  author = {Wale, Nikil and \textbf{X. Ning} and Karypis, George},
  title = {Trends in Chemical Graph Data Mining},
  booktitle = {Managing and Mining Graph Data},
  publisher = {Springer US},
  year = {2010},
  editor = {Elmagarmid, Ahmed K. and Aggarwal, Charu C. and Wang, Haixun},
  volume = {40},
  series = {Advances in Database Systems},
  pages = {581-606},
  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.},
  affiliation = {Computer Science & Engineering University of Minnesota Twin CitiesUS},
  file = {papers/Wale2010.pdf},
  isbn = {978-1-4419-6045-0},
  keyword = {Computer Science},
  keywords = {Chemical Graph, Descriptor Spaces, Classification, Ranked Retrieval,
	Scaffold Hopping, Target Fishing},
  url = {http://dx.doi.org/10.1007/978-1-4419-6045-0_19}
}