Linked Life Data

15669703

Source:http://linkedlifedata.com/resource/pubmed/id/15669703

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5-6
pubmed:dateCreated
2005-1-26
pubmed:abstractText
The present study focuses on fish antibiotics which are an important group of pharmaceuticals used in fish farming to treat infections and, until recently, most of them have been exposed to the environment with very little attention. Information about the environmental behaviour and the description of the environmental fate of medical substances are difficult or expensive to obtain. The experimental information in terms of properties is reported when available, in other cases, it is estimated by standard tools as those provided by the United States Environmental Protection Agency EPISuite software and by custom quantitative structure-activity relationship (QSAR) applications. In this study, a QSAR screening of 15 fish antibiotics and 132 xenobiotic molecules was performed with two aims: (i) to develop a model for the estimation of octanol--water partition coefficient (logP) and (ii) to estimate the relative binding affinity to oestrogen receptor (log RBA) using a model constructed on the activities of 132 xenobiotic compounds. The custom models are based on constitutional, topological, electrostatic and quantum chemical descriptors computed by the CODESSA software. Kohonen neural networks (self organising maps) were used to study similarity between the considered chemicals while counter-propagation artificial neural networks were used to estimate the properties.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1062-936X
pubmed:author
pubmed:issnType
Print
pubmed:volume
15
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
469-80
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:articleTitle
Application of counterpropagation artificial neural network for modelling properties of fish antibiotics.
pubmed:affiliation
Chemistry Department, University of Trieste, Trieste, Italy.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't