lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Global and local PLS regression models to predict vapor pressure
University of Kalmar, School of Pure and Applied Natural Sciences.ORCID iD: 0000-0001-9382-9296
University of Kalmar, School of Pure and Applied Natural Sciences.
2008 (English)In: QSAR & combinatorial science (Print), ISSN 1611-020X, E-ISSN 1611-0218, Vol. 27, no 3, 273-279 p.Article in journal (Refereed) Published
Abstract [en]

The vapor pressure is a key property in determining the distribution and fate of environmentally relevant compounds, but experimental determinations are only available for alimited number of the chemicals in current commercial use. Despite experimental efforts there is a need for estimation methods. The liquid or subcooled liquid vapor pressures at 298.15 K were collected from the literature for a diverse set of 1340 organic compounds. Theoretical molecular descriptors were derived after optimization to low-energy conformations and used to investigate the performance of global and local Quantitative Structure – Property Relationships (QSPR). A global PLSR model with ten latent variables was found to be optimal. The predictive performance of this model, within the domain of applicability, was estimated at n=420, Q2Ext0.980, and RMSEP=0.410 (log Pa). This model can be used in conjunction with other estimation models to assess the potential for a long range atmospheric transport.

 

Place, publisher, year, edition, pages
Weinhem: Wiley-VCH Verlag , 2008. Vol. 27, no 3, 273-279 p.
Keyword [en]
LFER, PLSR, External validation, Local regression, Molecular descriptors, Nonlinear modeling, Partial
National Category
Environmental Sciences
Research subject
Environmental Science, Environmental Chemistry
Identifiers
URN: urn:nbn:se:lnu:diva-8645DOI: 10.1002/qsar.200730038OAI: oai:DiVA.org:lnu-8645DiVA: diva2:353232
Available from: 2010-09-24 Created: 2010-09-24 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

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Öberg, TomasLiu, Tao
By organisation
School of Pure and Applied Natural Sciences
In the same journal
QSAR & combinatorial science (Print)
Environmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 120 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf