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dc.contributor.authorNicolaou, Christos A.en
dc.contributor.authorBrown, N.en
dc.contributor.authorPattichis, Constantinos S.en
dc.creatorNicolaou, Christos A.en
dc.creatorBrown, N.en
dc.creatorPattichis, Constantinos S.en
dc.date.accessioned2019-11-13T10:41:31Z
dc.date.available2019-11-13T10:41:31Z
dc.date.issued2007
dc.identifier.issn1367-6733
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54635
dc.description.abstractImproving the profile of a molecule for the drug-discovery process requires the simultaneous optimization of numerous, often competing objectives. Traditionally, standard chemo-informatics methods ignored this problem and focused on the sequential optimization of each single biological or chemical property (ie, a single objective). This approach, known as single-objective optimization (SOOP), strives to discover a single optimal solution to the optimization problem. Implicitly, SOOP-based methods assume that the optimal solution for an objective will also be the optimum for any other objectives involved in the profiling of a molecule. However, when these other objectives are conflicting, as is often the case in drug discovery, the individual optima corresponding to the numerous objectives may vary substantially. Multi-objective optimization (MOOP) methods introduce a new approach for gaining optimality based on compromises and trade-offs among the various objectives. MOOP aims to discover a set of satisfactory compromises that can in turn be used to discover the global optimal solution by optimizing numerous dependent properties simultaneously. MOOP methods have only recently been introduced to the field of chemoinformatics. This paper first presents a brief introduction to issues related to MOOP and then surveys the application of MOOP methods in the field of chemoinformatics. © The Thomson Corporation.en
dc.sourceCurrent Opinion in Drug Discovery and Developmenten
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34249109971&partnerID=40&md5=daaf3d37e13598f7e7cb8c04da66e082
dc.subjectComputer Simulationen
dc.subjectAlgorithmsen
dc.subjectalgorithmen
dc.subjectreviewen
dc.subjectdrug bioavailabilityen
dc.subjectthree dimensional imagingen
dc.subjectgenetic algorithmen
dc.subjectdrug designen
dc.subjectLigandsen
dc.subjectPharmaceutical Preparationsen
dc.subjectMolecular Structureen
dc.subjectInformaticsen
dc.subjectProteinsen
dc.subjectmolecular dockingen
dc.subjectProtein Bindingen
dc.subjectProtein Conformationen
dc.subjectBinding Sitesen
dc.subjectChemoinformaticsen
dc.subjectquantitative structure activity relationen
dc.subjectQuantitative Structure-Activity Relationshipen
dc.subjectModels, Molecularen
dc.subjectcomputer aided designen
dc.subjectComputer-Aided Designen
dc.subjectpharmacophoreen
dc.subjectquantitative structure property relationen
dc.subjectTechnology, Pharmaceuticalen
dc.subjectMulti-objective optimizationen
dc.subjectCombinatorial Chemistry Techniquesen
dc.subjectMulti-criterion optimizationen
dc.subjectMulti-objective evolutionary algorithmen
dc.titleMolecular optimization using computational multi-objective methodsen
dc.typeinfo:eu-repo/semantics/article
dc.description.volume10
dc.description.issue3
dc.description.startingpage316
dc.description.endingpage324
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :62</p>en
dc.source.abbreviationCurr.Opin.Drug Discov.Dev.en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidNicolaou, Christos A. [0000-0002-1466-6992]
dc.gnosis.orcid0000-0003-1271-8151
dc.gnosis.orcid0000-0002-1466-6992


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