Linked Life Data

10464009

Source:http://linkedlifedata.com/resource/pubmed/id/10464009

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
17
pubmed:dateCreated
1999-9-20
pubmed:abstractText
Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D(1) dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R(2) guided region selection (q(2)-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R(2) (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D(1) ligands.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
26
pubmed:volume
42
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3217-26
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Quantitative structure-activity relationship modeling of dopamine D(1) antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares, and K nearest neighbor methods.
pubmed:affiliation
Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
pubmed:publicationType
Journal Article, Comparative Study, In Vitro