Linked Life Data

10737746

Source:http://linkedlifedata.com/resource/pubmed/id/10737746

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2000-4-13
pubmed:abstractText
A new topological method that makes it possible to predict the properties of molecules on the basis of their chemical structures is applied in the present study to quinolone antimicrobial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. This makes it possible to determine the minimal inhibitory concentration (MIC) of quinolones. Analysis of the results shows that the experimental and calculated values are highly similar. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried out.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
23
pubmed:volume
43
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1143-8
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Artificial neural network applied to prediction of fluorquinolone antibacterial activity by topological methods.
pubmed:affiliation
Department of Physical Chemistry, University of València, Av. Vicent Andrés Estellés S/N, 46100 Burjassot, Valencia, Spain.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't