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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1998-4-28
pubmed:abstractText
The U.S. National Cancer Institute (NCI) conducts a drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs of origin. Since 1990, approximately 63,000 compounds have been tested, and their patterns of activity profiled. Recently, we analyzed the antitumor activity patterns of 112 ellipticine analogues using a hierarchical clustering algorithm. Dramatic coherence between molecular structures and activity patterns was observed qualitatively from the cluster tree. In the present study, we further investigate the quantitative structure-activity relationships (QSAR) of these compounds, in particular with respect to the influence of p53-status and the CNS cell selectivity of the activity patterns. Independent variables (i.e., chemical structural descriptors of the ellipticine analogues) were calculated from the Cerius2 molecular modeling package. Important structural descriptors, including partial atomic charges on the ellipticine ring-forming atoms, were identified by the recently developed genetic function approximation (GFA) method. For our data set, the GFA method gave better correlation and cross-validation results (R2 and CVR2 were usually approximately 0.3 higher) than did classical stepwise linear regression. A procedure for improving the performance of GFA is proposed, and the relative advantages and disadvantages of using GFA for QSAR studies are discussed.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0095-2338
pubmed:author
pubmed:issnType
Print
pubmed:volume
38
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
189-99
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:articleTitle
Mining the NCI anticancer drug discovery databases: genetic function approximation for the QSAR study of anticancer ellipticine analogues.
pubmed:affiliation
Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
pubmed:publicationType
Journal Article