Linked Life Data

17300970

Source:http://linkedlifedata.com/resource/pubmed/id/17300970

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-10-8
pubmed:abstractText
We investigate the possible use of families of momentum-space descriptors and of trivial classical descriptors for the prediction of blood-brain barrier penetration, expressed as log BB. A 12-descriptor model based on entropy-like momentum-space quantities and on the numbers of atoms of each type has good statistical quality for a set of 42 structurally diverse molecules. We also consider the inclusion in our models of some of the other descriptors that have been used in earlier models for these molecules. The resulting models are not expected to be useful as-is for making genuine predictions for much larger test sets, but the various results do demonstrate the potential benefits of incorporating momentum-space descriptors into QSAR models for predicting logBB.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1093-3263
pubmed:author
pubmed:issnType
Print
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
607-12
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Investigating the utility of momentum-space descriptors for predicting blood-brain barrier penetration.
pubmed:affiliation
Department of Chemistry, University of Liverpool, Liverpool L69 7ZD, UK.
pubmed:publicationType
Journal Article