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QSAR models for prediction of PPARδ agonistic activity of indanylacetic acid derivatives

2009Journal article Open access
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dc.contributor.authorLather, Viney
dc.contributor.authorFernandes, Miguel X.
dc.date.accessioned2023-02-08T10:32:23Z
dc.date.available2023-02-08T10:32:23Z
dc.date.issued2009
dc.description.abstractPeroxisome Proliferator Activated Receptor b/d (PPAR b/d), one of three PPAR isoforms is a member of nuclear receptor superfamily and ubiquitously expressed in several metabolically active tissues such as liver, muscle, and fat. Tissue specific expression and knock-out studies suggest a role of PPARd in obesity and metabolic syndrome. Specific and selective PPARd ligands may play an important role in the treatment of metabolic disorders. Indanylacetic acid derivatives reported as potent and specific ligands against PPARd have been studied for the Quantitative Structure – Activity Relationships (QSAR). Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. Four different approaches, i.e., random selection, hierarchical clustering, k-means clustering, and sphere exclusion method were used to classify the dataset into training and test subsets. Forward stepwise Multiple Linear Regression (MLR) approach was used to linearly select the subset of descriptors and establish the linear relationship with PPARd agonistic activity of the molecules. The models were validated internally by Leave One Out (LOO) and externally for the prediction of test sets. The best subset of descriptors was then fed to the Artificial Neural Networks (ANN) to develop non-linear models. Statistically significant MLR models; with R2 varying from 0.80 to 0.87 were generated based on the different training and test set selection methods. Training of ANNs with different architectures for the different training and test selection methods resulted in models with R2 values varying from 0.83 to 0.94, which indicates the high predictive ability of the models.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationLather, V., & Fernandes, M. X. (2009). QSAR models for prediction of PPARδ agonistic activity of indanylacetic acid derivatives. QSAR & Combinatorial Science, 28(4), 447-457.pt_PT
dc.identifier.doi10.1002/qsar.200810092pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5012
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWileypt_PT
dc.relationDESIGN AND DEVELOPMENT OF PPARDELTA SELECTIVE LIGANDS USING STRUCTURE BASED AND LIGAND BASED COMPUTATIONAL APPROACHES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectArtificial neural networkpt_PT
dc.subjectIndanylacetic acidspt_PT
dc.subjectMultiple linear regression (MLR)pt_PT
dc.subjectPeroxisome proliferator activated receptor (PPAR)pt_PT
dc.subjectQSARpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleQSAR models for prediction of PPARδ agonistic activity of indanylacetic acid derivativespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleDESIGN AND DEVELOPMENT OF PPARDELTA SELECTIVE LIGANDS USING STRUCTURE BASED AND LIGAND BASED COMPUTATIONAL APPROACHES
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/PIDDAC/SFRH%2FBPD%2F30954%2F2006/PT
oaire.citation.endPage457pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage447pt_PT
oaire.citation.titleQSAR & Combinatorial Sciencept_PT
oaire.citation.volume28pt_PT
oaire.fundingStreamPIDDAC
person.familyNameFernandes
person.givenNameMiguel Xavier
person.identifier.ciencia-idED1D-3C7A-467C
person.identifier.orcid0000-0002-1840-616X
person.identifier.ridA-4373-2013
person.identifier.scopus-author-id35466972500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery8dab9a0d-f44a-4d2d-b9b1-7b3145162ca3
relation.isProjectOfPublication5bbc25c3-10da-4766-8031-e547ff90b8de
relation.isProjectOfPublication.latestForDiscovery5bbc25c3-10da-4766-8031-e547ff90b8de

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