Linked Life Data

10816012

Source:http://linkedlifedata.com/resource/pubmed/id/10816012

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3-4
pubmed:dateCreated
2000-6-15
pubmed:abstractText
In our continuing efforts to provide a predictive toxicology capability, we seek to improve QSARs (quantitative structure-activity relationships) for chemicals of interest. Currently, although semi-empirical molecular orbital methods are hardly the state of the art for studying small molecules, AM1 calculations appear to be the method of choice when calculating quantum-chemical descriptors. However, with the advent of modern computational capabilities and the development of fast algorithms, ab initio molecular orbital and first principles density functional methods can be expeditiously applied in current QSAR studies. We present a study on halogenated alkanes to assess whether more accurate quantum methods result in QSARs that correlate better with experimental data. Furthermore, improved QSARs can also be obtained through development of new descriptors with explicit physical interpretations that should lead to better understanding of the mechanisms involved in the toxic response. We show that descriptors calculated from chemical intermediates may be useful in future QSARs.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0097-8485
pubmed:author
pubmed:issnType
Print
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
421-7
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Improved QSARs for predictive toxicology of halogenated hydrocarbons.
pubmed:affiliation
Technical Management Concepts, Beavercreek, OH 45434-3405, USA. steven.trohalaki@afrl.af.mil
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.