Linked Life Data

19406638

Source:http://linkedlifedata.com/resource/pubmed/id/19406638

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2009-5-15
pubmed:abstractText
Chemical database design is an important consideration for screening processes in drug discovery. More specifically, classification of a diverse compound set deeply influences the validation and the predictive power of prediction model for the designing of novel compounds. In this work, we investigated the effect of the reasonable classification on the prediction model. We first collected the known Cannabinoid-1 receptor antagonists. Following this, we calculate the chemical descriptors in order to classify the collected compounds. Finally, we build two predictive models via the 3D-QSAR using different molecular alignment and the alignment independent Molecular Interaction Field models.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1464-3405
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2990-6
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Predictive models of Cannabinoid-1 receptor antagonists derived from diverse classes.
pubmed:affiliation
Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology, Yuseong-gu, Daejeon, Republic of Korea. nskang@krict.re.kr
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't