Linked Life Data

18767832

Source:http://linkedlifedata.com/resource/pubmed/id/18767832

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
2008-9-23
pubmed:abstractText
Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1549-9596
pubmed:author
pubmed:issnType
Print
pubmed:volume
48
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1868-81
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.
pubmed:affiliation
School for Electrical Engineering and Computer Science, Berlin Institute of Technology, Berlin, Germany. johann@cs.tu-berlin.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't