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dc.contributor.author
Andrada, Matias Fernando
dc.contributor.author
Vega Hissi, Esteban Gabriel
dc.contributor.author
Estrada, Mario Rinaldo
dc.contributor.author
Garro Martinez, Juan Ceferino
dc.date.available
2018-09-20T17:03:29Z
dc.date.issued
2015-04
dc.identifier.citation
Andrada, Matias Fernando; Vega Hissi, Esteban Gabriel; Estrada, Mario Rinaldo; Garro Martinez, Juan Ceferino; Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 143; 4-2015; 122-129
dc.identifier.issn
0169-7439
dc.identifier.uri
http://hdl.handle.net/11336/60452
dc.description.abstract
In this work, we performed a quantitative structure activity relationship (QSAR) model for a family of 5-lipoxygenase (5-LOX) inhibitors using k-means clustering and linear discriminant analysis (LDA) for the selection of training and test sets and multivariate linear regression (MLR) for the independent variable selection. With the k-means clustering method, the total set of compounds (58 derivatives of 5-Benzylidene-2-phenylthiazolinones) was divided in two clusters according to a simple discriminant function. We found that piID (conventional bond order ID number) molecular descriptor discriminates correctly 100% of the compounds of each clusters. Thirty different models divided in three series were analyzed and the series with representative training and test sets (series 3) had the most predictive models. The statistical parameters of the best model are Rtrain=0.811 and Rtest=0.801. We found that a rational selection in the setting-up of training and test sets allows to obtain the most predictive models and the random selection is sometimes unsuitable, especially, when the total set of compounds can be classified in different clusters according to structural features.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
5-Lipoxygenase Inhibitors
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K-Means Clustering
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Linear Discriminant Analysis
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Multivariate Linear Regression
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Qsar
dc.subject.classification
Otras Ciencias Químicas
dc.subject.classification
Ciencias Químicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-09-20T13:12:55Z
dc.journal.volume
143
dc.journal.pagination
122-129
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Andrada, Matias Fernando. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Vega Hissi, Esteban Gabriel. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Estrada, Mario Rinaldo. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina
dc.description.fil
Fil: Garro Martinez, Juan Ceferino. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Chemometrics and Intelligent Laboratory Systems
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.chemolab.2015.03.001
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743915000593