A novel QSPR model for predicting θ (lower critical solution temperature) in polymer solutions using molecular descriptors

Melagraki, G; Afantitis, A; Sarimveis, H; Koutentis, PA; Markopoulos, J; Igglessi-Markopoulou, O

dc.contributor.author	Melagraki, G	en
dc.contributor.author	Afantitis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Koutentis, PA	en
dc.contributor.author	Markopoulos, J	en
dc.contributor.author	Igglessi-Markopoulou, O	en
dc.date.accessioned	2014-03-01T01:25:45Z
dc.date.available	2014-03-01T01:25:45Z
dc.date.issued	2007	en
dc.identifier.issn	1610-2940	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17747
dc.subject	Lower critical solution temperature	en
dc.subject	Molecular de scriptors	en
dc.subject	QSPR	en
dc.subject.classification	Biochemistry & Molecular Biology	en
dc.subject.classification	Biophysics	en
dc.subject.classification	Chemistry, Multidisciplinary	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.other	polymer	en
dc.subject.other	solvent	en
dc.subject.other	accuracy	en
dc.subject.other	article	en
dc.subject.other	chemical model	en
dc.subject.other	correlation analysis	en
dc.subject.other	data base	en
dc.subject.other	elimination reaction	en
dc.subject.other	mathematical analysis	en
dc.subject.other	physical chemistry	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	quantitative structure property relation	en
dc.subject.other	regression analysis	en
dc.subject.other	temperature	en
dc.subject.other	validation process	en
dc.title	A novel QSPR model for predicting θ (lower critical solution temperature) in polymer solutions using molecular descriptors	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00894-006-0125-z	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00894-006-0125-z	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In this study, we present a new model that has been developed for the prediction of θ, (lower critical solution temperature) using a database of 169 data points that include 12 polymers and 67 solvents. For the characterization of polymer and solvent molecules, a number of molecular descriptors (topological, physicochemical, steric and electronic) were examined. The best subset of descriptors was selected using the elimination selection-stepwise regression method. Multiple linear regression (MLR) served as the statistical tool to explore the potential correlation among the molecular descriptors and the experimental data. The prediction accuracy of the MLR model was tested using the leave-one-out cross-validation procedure, validation through an external test set and the Y-randomization evaluation technique. The domain of applicability was finally determined to identify the reliable predictions. © Springer-Verlag 2006.	en
heal.publisher	SPRINGER	en
heal.journalName	Journal of Molecular Modeling	en
dc.identifier.doi	10.1007/s00894-006-0125-z	en
dc.identifier.isi	ISI:000242326700007	en
dc.identifier.volume	13	en
dc.identifier.issue	1	en
dc.identifier.spage	55	en
dc.identifier.epage	64	en