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A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs

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dc.contributor.author Afantitis, A en
dc.contributor.author Melagraki, G en
dc.contributor.author Sarimveis, H en
dc.contributor.author Koutentis, PA en
dc.contributor.author Igglessi-Markopoulou, O en
dc.contributor.author Kollias, G en
dc.date.accessioned 2014-03-01T01:32:25Z
dc.date.available 2014-03-01T01:32:25Z
dc.date.issued 2010 en
dc.identifier.issn 1381-1991 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/20126
dc.subject CXCR3 en
dc.subject In silico predictions en
dc.subject Molecular modelling en
dc.subject QSAR en
dc.subject.classification Biochemistry & Molecular Biology en
dc.subject.classification Chemistry, Applied en
dc.subject.classification Chemistry, Medicinal en
dc.subject.classification Chemistry, Multidisciplinary en
dc.subject.other antiinflammatory agent en
dc.subject.other chemokine receptor CXCR3 en
dc.subject.other quinazolinone derivative en
dc.subject.other article en
dc.subject.other computer model en
dc.subject.other IC 50 en
dc.subject.other kernel method en
dc.subject.other least squares support vector machine en
dc.subject.other molecular model en
dc.subject.other pharmacophore en
dc.subject.other prediction en
dc.subject.other priority journal en
dc.subject.other quantitative structure activity relation en
dc.subject.other radial based function en
dc.subject.other receptor blocking en
dc.subject.other sensitivity and specificity en
dc.subject.other support vector machine en
dc.subject.other validation study en
dc.subject.other algorithm en
dc.subject.other chemical model en
dc.subject.other chemistry en
dc.subject.other drug antagonism en
dc.subject.other regression analysis en
dc.subject.other reproducibility en
dc.subject.other statistical model en
dc.subject.other Algorithms en
dc.subject.other Inhibitory Concentration 50 en
dc.subject.other Least-Squares Analysis en
dc.subject.other Linear Models en
dc.subject.other Models, Chemical en
dc.subject.other Quantitative Structure-Activity Relationship en
dc.subject.other Quinazolinones en
dc.subject.other Receptors, CXCR3 en
dc.subject.other Reproducibility of Results en
dc.title A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs en
heal.type journalArticle en
heal.identifier.primary 10.1007/s11030-009-9163-7 en
heal.identifier.secondary http://dx.doi.org/10.1007/s11030-009-9163-7 en
heal.language English en
heal.publicationDate 2010 en
heal.abstract A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "nonactive" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC50 inhibition values. The accuracy of the QSAR model (R-2 = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure (R-LOO(2) = 0.67) and validation through an external test set (R-pred(2) = 0.78). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy ( HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1)kappa), demonstrate discriminatory and pharmacophore abilities. en
heal.publisher SPRINGER en
heal.journalName Molecular Diversity en
dc.identifier.doi 10.1007/s11030-009-9163-7 en
dc.identifier.isi ISI:000276521000003 en
dc.identifier.volume 14 en
dc.identifier.issue 2 en
dc.identifier.spage 225 en
dc.identifier.epage 235 en


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