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Open Access Green as soon as Postprint is submitted to ZB.
CADASTER QSPR models for predictions of melting and boiling points of perfluorinated chemicals.
Mol. Inform. 30, 189-204 (2011)
Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EUREACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Perfluorinated chemicals (PFCs); Quantitative structure property relationship (QSPR); Multiple linear regression (MLR); Partial least squares regression (PLSR); Neural network (NN); ASSOCIATIVE NEURAL-NETWORK; APPLICABILITY DOMAIN; TRAINING SET; QSAR MODELS; PLS-REGRESSION; VAPOR-PRESSURE; TOXICITY; VALIDATION; SELECTION; ACID
ISSN (print) / ISBN
1868-1743
e-ISSN
1868-1751
Journal
Molecular Informatics
Quellenangaben
Volume: 30,
Issue: 2-3,
Pages: 189-204
Publisher
Wiley
Publishing Place
Weinheim
Non-patent literature
Publications
Reviewing status
Peer reviewed