Linked Life Data

12678788

Source:http://linkedlifedata.com/resource/pubmed/id/12678788

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2003-4-7
pubmed:abstractText
The dramatically increasing number of compounds that become available for biological evaluation presents a significant challenge for database design, management, and mining. Computational approaches for screening, profiling, or filtering of large compound collections are by now widely used in pharmaceutical research. Among popular compound classification and database mining techniques, partitioning methods are computationally very efficient and particularly suitable for the analysis of increasingly large molecular databases, as they do not depend on pair-wise comparisons of compounds to assess molecular similarity or diversity. Promising applications of partitioning algorithms include diversity selection, searching for compounds with desired biological activity, or the derivation of predictive models from screening datasets. Compound partitioning is introduced here in the context of virtual screening and different partitioning methods are discussed that operate in low-dimensional or other chemical descriptor spaces, including a number of practical drug-discovery-related applications.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0929-8673
pubmed:author
pubmed:issnType
Print
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
707-15
pubmed:dateRevised
2007-2-12
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Partitioning methods for the identification of active molecules.
pubmed:affiliation
Department of Computer-Aided Drug Discovery, Albany Molecular Research Inc, Bothell, WA 98011, USA.
pubmed:publicationType
Journal Article, Review