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dc.contributor.authorMelagraki, G.en
dc.contributor.authorAfantitis, Antreasen
dc.contributor.authorSarimveis, H.en
dc.contributor.authorKoutentis, Panayiotis Andreasen
dc.contributor.authorMarkopoulos, J.en
dc.contributor.authorIgglessi-Markopoulou, O.en
dc.creatorMelagraki, G.en
dc.creatorAfantitis, Antreasen
dc.creatorSarimveis, H.en
dc.creatorKoutentis, Panayiotis Andreasen
dc.creatorMarkopoulos, J.en
dc.creatorIgglessi-Markopoulou, O.en
dc.date.accessioned2019-11-21T06:21:26Z
dc.date.available2019-11-21T06:21:26Z
dc.date.issued2007
dc.identifier.issn0920-654X
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/55847
dc.description.abstractThis paper presents the results of an optimization study on biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists, which was accomplished by using quantitative-structure activity relationships (QSARs), classification and virtual screening techniques. First, a linear QSAR model was developed using Multiple Linear Regression (MLR) Analysis, while the Elimination Selection-Stepwise Regression (ES-SWR) method was adopted for selecting the most suitable input variables. The predictive activity of the model was evaluated using an external validation set and the Y-randomization technique. Based on the selected descriptors, the Support Vector Machines (SVM) classification technique was utilized to classify data into two categories: "actives" or "non-actives". Several attempts were made to optimize the scaffold of most potent compounds by inducing various structural modifications. Potential derivatives with improved activities were identified, as they were classified "actives" by the SVM classifier. Their activities were estimated using the produced MLR model. A detailed analysis on the model applicability domain defined the compounds, whose estimations can be accepted with confidence. © Springer Science+Business Media, LLC 2007.en
dc.sourceJournal of computer-aided molecular designen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34247340197&doi=10.1007%2fs10822-007-9112-4&partnerID=40&md5=fd9fe08afa18693d93ce734116ab3345
dc.subjectarticleen
dc.subjectcontrolled studyen
dc.subjectpredictionen
dc.subjectpriority journalen
dc.subjectunclassified drugen
dc.subjectdrug screeningen
dc.subjectbinding affinityen
dc.subjectDrug Designen
dc.subjectClassificationen
dc.subjectmathematical analysisen
dc.subjectprocess optimizationen
dc.subjectdrug potencyen
dc.subjectQSARen
dc.subjectquantitative structure activity relationen
dc.subjectQuantitative Structure-Activity Relationshipen
dc.subjectVirtual screeningen
dc.subjectModels, Molecularen
dc.subjectdrug structureen
dc.subjectsupport vector machineen
dc.subjectmultiple linear regression analysisen
dc.subjectPiperidinesen
dc.subjectUreaen
dc.subject4 amino 2 biarylureaen
dc.subjectbiaryl piperidineen
dc.subjectdrug classificationen
dc.subjectexternal validityen
dc.subjecthormone receptor blocking agenten
dc.subjectMCH1Ren
dc.subjectmelanin concentrating hormone receptor blocking agenten
dc.subjectpiperidine derivativeen
dc.subjectReceptors, Somatostatinen
dc.subjectSVMen
dc.subjecttetrahydroisoquinolineen
dc.subjecturea derivativeen
dc.titleOptimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screeningen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1007/s10822-007-9112-4
dc.description.volume21
dc.description.issue5
dc.description.startingpage251
dc.description.endingpage267
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Χημείας / Department of Chemistry
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :28</p>en
dc.source.abbreviationJ.Comp.-Aided Mol.Des.en
dc.contributor.orcidKoutentis, Panayiotis Andreas [0000-0002-4652-7567]
dc.contributor.orcidAfantitis, Antreas [0000-0002-0977-8180]
dc.contributor.orcidIgglessi-Markopoulou, O. [0000-0002-7683-8526]
dc.gnosis.orcid0000-0002-4652-7567
dc.gnosis.orcid0000-0002-0977-8180|0000-0002-7683-8526


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