Linked Life Data

18506591

Source:http://linkedlifedata.com/resource/pubmed/id/18506591

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-6-23
pubmed:abstractText
Substructures are among the most preferred molecular descriptors in chemoinformatics and medicinal chemistry. Conventional substructure-type descriptors are typically the result of well-defined design strategies. Previously, we have introduced Activity Class Characteristic Substructures (ACCS) derived from randomly generated molecular fragment populations and described their utility in similarity searching. Short ACCS fingerprints were found to perform surprisingly well on many compound classes when compared to more complex state-of-the-art 2D fingerprints. In order to elucidate potential reasons for the high predictive utility of ACCS, we have carried out a thorough analysis of their distribution in nine activity classes and nearly four million database compounds. We show that the discriminatory power of ACCS results from the rare occurrence of ACCS combinations in screening databases.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1381-1991
pubmed:author
pubmed:issnType
Print
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
77-83
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Distribution of randomly generated activity class characteristic substructures in diverse active and database compounds.
pubmed:affiliation
Department of Life Science Informatics, B-IT, LIMES Institute, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, 53113, Bonn, Germany.
pubmed:publicationType
Journal Article