Linked Life Data

10022323

Source:http://linkedlifedata.com/resource/pubmed/id/10022323

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
1999-2-25
pubmed:abstractText
Structure Activity Relationships (SARs) or Quantitative Structure Activity Relationships (QSARs) form the basis of most computer prediction systems in toxicology. The underlying premise of SARs and QSARs is that the properties of a chemical are implicit in its molecular structure. For an SAR or QSAR to be valid and reliable, the dependent property for all of the chemicals covered by the relationship has to be elicited by a mechanism which is both common to the set of chemicals as well as relevant to that dependent property. Similar principles must also be applied to the development of in vitro alternatives to animal tests if those methods are to be reliable. A number of ways in which computer prediction systems and in vitro toxicology can complement each other in the development of alternatives to live animal experiments are described.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0378-4274
pubmed:author
pubmed:issnType
Print
pubmed:day
28
pubmed:volume
102-103
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
617-21
pubmed:dateRevised
2003-11-14
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Integrating computer prediction systems with in vitro methods towards a better understanding of toxicology.
pubmed:affiliation
Marlin Consultancy, Carlton, Bedford, UK. martin.d.barratt@btinternet.com
pubmed:publicationType
Journal Article