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CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals
University of Insubria, Italy.
German Research Center for Environmental Health, Germany.
Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.ORCID iD: 0000-0001-9382-9296
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2011 (English)In: Molecular Informatics, ISSN 1868-1751, Vol. 30, no 2-3, 189-204 p.Article in journal (Refereed) Published
Abstract [en]

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 EU-REACH. 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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2011. Vol. 30, no 2-3, 189-204 p.
Keyword [en]
Perfluorinated chemicals (PFCs), Quantitative structure property relationship (QSPR), Multiple linear regression (MLR), Partial least squares regression (PLSR), Neural network (NN)
National Category
Chemical Sciences
Research subject
Natural Science, Environmental Science
Identifiers
URN: urn:nbn:se:lnu:diva-18727DOI: 10.1002/minf.201000133ISI: 000288861600012OAI: oai:DiVA.org:lnu-18727DiVA: diva2:527499
Available from: 2012-05-21 Created: 2012-05-21 Last updated: 2016-11-15Bibliographically approved
In thesis
1. Chemoinformetics for green chemistry
Open this publication in new window or tab >>Chemoinformetics for green chemistry
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis focuses on the development of quantitative structure-activity relationship (QSPR) models for physicochemical properties, e.g., vapor pressure and partitioning coefficients. Such models can be used to estimate environmental distribution and transformation of the pollutants or to characterize solvents properties. Here, chemoinformatics was used as an efficient tool for modeling to produce safe chemicals based on green chemistry principles.

Experimental determinations are only available for a limited number of the chemicals; however, theoretical molecular descriptors can be used for modeling of all organic compounds. In this thesis, we developed and validated a global and local QSPR model for vapor pressure of liquid and subcooled liquid organic compounds, in which perfluorinated compounds (PFCs) as outliers appeared in the model due to their molecular properties. Subsequently, after the update of the previous model, the vapor pressure of perfluorinated compounds (PFCs) for which no reliable experimental data are available was successfully predicted. At the same time, we used partitioning between n-octanol/water (Kow) and water solubility (Sw) to investigate the similarities and differences between linear solvation energy relationship (LSER) and partial least square projection to latent structures (PLS) models. Further, we developed QSPR model for prediction of melting points and boiling points of PFCs using multiple linear regression (MLR), PLS and associative neural networks (ASNN) approaches, meanwhile, the applicability domain of PFCs was also investigated.

Experimental, semi-empirical and theoretical quantitative structure-retention relationship (QSRR) models were used to accurately predict retention factors (logk) in reversed-phase liquid chromatography (RPLC). These models are useful to characterize solvents for determination of the behavior and interactions of molecular structure and develop chromatographic methods. In both of QSPR and QSRR models using the PLS method, the first and second components captured main information which is related to van der Waals forces and polar interactions, and their results coincide with those from LSER.

The results showed that the models of physicochemical properties and retention factors (logk) in chromatographic system can be successfully developed by the PLS method. PLS models were able to predict physicochemical properties of organic compounds directly from theoretical descriptors without prior synthesis, measurement or sampling. Further, the PLS method could overcome colinearity in data sets, and it is therefore a rapid, cheap and highly efficient approach

Place, publisher, year, edition, pages
Växjö, Kalmar: Linnaeus University Press, 2010. 164 p.
Series
Linnaeus University Dissertations, 26/2010
Keyword
QSPR, PLS, Chemometrics, Chemoinformatics
National Category
Environmental Sciences
Research subject
Environmental Science, Environmental Chemistry
Identifiers
urn:nbn:se:lnu:diva-8634 (URN)978-91-86491-40-6 (ISBN)
Public defence
2010-10-22, A137, Landgången 4, Kocken, 09:30 (English)
Opponent
Supervisors
Available from: 2010-10-26 Created: 2010-09-24 Last updated: 2013-11-04Bibliographically approved

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