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dc.contributor.author
Soto, Axel Juan
dc.contributor.author
Strickert, Marc
dc.contributor.author
Vazquez, Gustavo Esteban
dc.date.available
2018-10-12T15:25:50Z
dc.date.issued
2010-03
dc.identifier.citation
Soto, Axel Juan; Strickert, Marc; Vazquez, Gustavo Esteban; Adaptive matrix metrics for molecular descriptor assessment in QSPR classification; Chemistry Central; Journal of Cheminformatics; 2; s1; 3-2010; 47-47
dc.identifier.issn
1758-2946
dc.identifier.uri
http://hdl.handle.net/11336/62306
dc.description.abstract
QSPR methods represent a useful approach in the drug discovery process, since they allow to predict in advance biological or physicochemical properties of a candidate drug. For this goal, it is necessary that the QSPR method be as accurate as possible to provide reliable predictions. Moreover, the selection of the molecular descriptors is an important task to create QSPR prediction models of low complexity which, at the same time, provide accurate predictions. In this work, a matrix-based method is used to transform the original data space of chemical compounds into an alternative space where compounds with different target properties can be better separated. For using this approach, QSPR is considered as a classification problem. The advantage of using adaptive matrix metrics is twofold: it can be used to identify important molecular descriptors and at the same time it allows improving the classification accuracy. A recently proposed method making use of this concept is extended to multi-class data. The new method is related to linear discriminant analysis and shows better results at yet higher computational costs. An application for relating chemical descriptors to hydrophobicity property shows promising results.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Chemistry Central
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Adaptive Matrix Metrics
dc.subject
Qsar
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-09-18T15:02:09Z
dc.journal.volume
2
dc.journal.number
s1
dc.journal.pagination
47-47
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
dc.description.fil
Fil: Strickert, Marc. Leibniz Institute of Plant Genetics and Crop Plant Research; Alemania
dc.description.fil
Fil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
dc.journal.title
Journal of Cheminformatics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1186/1758-2946-2-S1-P47
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-2-S1-P47
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