Linked Life Data

18211009

Source:http://linkedlifedata.com/resource/pubmed/id/18211009

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-2-8
pubmed:abstractText
Four different models are used to predict whether a compound will bind to 2C9 with a K(i) value of less than 10 microM. A training set of 276 compounds and a diverse validation set of 50 compounds were used to build and assess each model. The modeling methods are chosen to exploit the differences in how training sets are used to develop the predictive models. Two of the four methods develop partitioning trees based on global descriptions of structure using nine descriptors. A third method uses the same descriptors to develop local descriptions that relate activity to structures with similar descriptor characteristics. The fourth method uses a graph-theoretic approach to predict activity based on molecular structure. When all of these methods agree, the predictive accuracy is 94%. An external validation set of 11 compounds gives a predictive accuracy of 91% when all methods agree.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
14
pubmed:volume
51
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
648-54
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
High confidence predictions of drug-drug interactions: predicting affinities for cytochrome P450 2C9 with multiple computational methods.
pubmed:affiliation
Department of Mathematics, Washington State University, Pullman, WA 99164-3113, USA. mhudelson@wsu.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural