Linked Life Data

20547432

Source:http://linkedlifedata.com/resource/pubmed/id/20547432

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
2010-8-9
pubmed:abstractText
Four molecular descriptors were selected from a pool of variables using genetic algorithm, and then used to built a QSAR model for a series of 1-(azacyclyl)-3-arylsulfonyl-1H-pyrrolo[2,3-b]pyridines as 5-HT(6) receptor agonists or antagonists, useful for the treatment of central nervous system disorders. Simple multiple linear regression (MLR) and a nonlinear method, artificial neural network (ANN), were used to model the bioactivities of the compounds; while MLR gave an acceptable model for predictions, the ANN-based model improved significantly the predictive ability, being more reliable for the prediction and design of novel 5-HT(6) receptor ligands. Topology and molecular/group sizes are important requirements to take into account during the development of novel analogs.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1768-3254
pubmed:author
pubmed:copyrightInfo
2010 Elsevier Masson SAS. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
45
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3911-5
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
QSAR studies of bioactivities of 1-(azacyclyl)-3-arylsulfonyl-1H-pyrrolo[2,3-b]pyridines as 5-HT6 receptor ligands using physicochemical descriptors and MLR and ANN-modeling.
pubmed:affiliation
Department of Chemistry, Faculty of Sciences, Islamic Azad University, Arak Branch, Arak, Markazi, Iran.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't