Linked Life Data

19497644

Source:http://linkedlifedata.com/resource/pubmed/id/19497644

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2009-8-3
pubmed:abstractText
A three-class support vector classification (SVC) model with high prediction accuracy for the training, test and overall data sets (95.2%, 88.6% and 93.1%, respectively) was developed based on the molecular descriptors of 148 Akt/protein kinase B (PKB) inhibitors. Then, support vector regression (SVR) method was applied to set up a more accurate model with good correlation coefficient (r(2)) for the training, test and overall data sets (0.882, 0.762 and 0.840, respectively). Enrichment factors (EF) and receiver operating curves (ROC) studies of database screening were also performed either using the SVR model alone or assisted with the SVC model, the results of which demonstrated that the established models could be useful and reliable tools in identifying structurally diverse compounds with Akt inhibitory activity.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1768-3254
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
44
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4090-7
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
QSAR study of Akt/protein kinase B (PKB) inhibitors using support vector machine.
pubmed:affiliation
ZJU-ENS Joint Laboratory of Medicinal Chemistry, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't