Linked Life Data

15272857

Source:http://linkedlifedata.com/resource/pubmed/id/15272857

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2004-7-26
pubmed:abstractText
This paper develops a quantitative k-nearest neighbors modeling technique. The technique is used to demonstrate that a compound's biological binding activity to a receptor can be calculated from the minimum of the square root of the sum of squared deviations (SSSD) of a structurally assigned chemical shift on a template between the unknown compound to be predicted and a set of known compounds with known activities. When building models of biological activity, nonlinear relationships are built into the input training data. If a model is developed by selecting only compounds with minimum structurally assigned chemical shift deviations from the unknown compound, some of the nonlinear relationships can be removed. The smaller the total chemical shift deviation between a compound with known activity and another compound with unknown activity, the more likely it will have similar biological, chemical, and physical properties. This means that a model can be produced without rigorous statistics or neural networks. This technique is similar to structure-activity relationship (SAR) modeling, but instead of relying on substructure fragments to produce a model, this new model is based on minimum chemical shift differences on those substructure fragments. We refer to this method as minimum deviation of structurally assigned spectra analysis (MiDSASA) modeling. Modeling by the minimum deviation concept can be applied to other chemoinformatic data analyses such as metabolite concentrations in metabolic pathways for metabolomics research. A MiDSASA template model for 30 steroids binding the corticosterone binding globulin based on the activity factors of the two nearest compounds had a correlation of 0.88. A MiDSASA template model for 50 steroids binding the aromatse enzyme based on the average activity of the four nearest compounds had a correlation of 0.71.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0095-2338
pubmed:author
pubmed:issnType
Print
pubmed:volume
44
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1489-96
pubmed:meshHeading
pubmed:articleTitle
Models of steroid binding based on the minimum deviation of structurally assigned 13C NMR spectra analysis (MiDSASA).
pubmed:affiliation
Division of Chemistry, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079-9502, USA. rberger@nctr.fda.gov
pubmed:publicationType
Journal Article