Linked Life Data

8978853

Source:http://linkedlifedata.com/resource/pubmed/id/8978853

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
26
pubmed:dateCreated
1997-1-24
pubmed:abstractText
A novel tool, called a genetic neural network (GNN), has been developed for obtaining quantitative structure-activity relationships (QSAR) for high-dimensional data sets (J. Med. Chem. 1996, 39, 1521-1530). The GNN method uses a neural network to correlate activity with descriptors that are preselected by a genetic algorithm. To provide an extended test of the GNN method, the data on 57 benzodiazepines given by Maddalena and Johnston (MJ; J. Med. Chem. 1995, 38, 715-724) have been examined with an enhanced version of GNN, and the results are compared with the excellent QSAR of MJ. The problematic steepest descent training has been replaced by the scaled conjugate gradient algorithm. This leads to a substantial gain in performance in both robustness of prediction and speed of computation. The cross-validation GNN simulation and the subsequent run based on an unbiased and more efficient protocol led to the discovery of other 10-descriptor QSARs that are superior to the best model of MJ based on backward elimination selection and neural network training. Results from a series of GNNs with a different number of inputs showed that a neural network with fewer inputs can produce QSARs as good as or even better than those with higher dimensions. The top-ranking models from a GNN simulation using only six input descriptors are presented, and the chemical significance of the chosen descriptors is discussed. The statistical significance of these GNN QSARs is validated. The best QSARs are used to provide a graphical tool that aids the design of new drug analogues. By replacing functional groups at the 7- and 2'-positions with ones that have optimal substituent parameters, a number of new benzodiazepines with high potency are predicted.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
20
pubmed:volume
39
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5246-56
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors.
pubmed:affiliation
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.