61
Views
6
CrossRef citations to date
0
Altmetric
Original Articles

Predicting thermochemical parameters of oxygen-containing heterocycles using simple QSPR models

, , &
Pages 125-134 | Received 01 Nov 2006, Accepted 01 Nov 2006, Published online: 31 Jan 2007
 

Abstract

Quantitative structure–property relationships for the prediction of standard enthalpies and entropies of formation as well as standard molar heat capacities for small oxygen heterocyclic compounds were developed, using 1D, 2D and 3D descriptors and experimental or computed thermochemical data. To develop the models, the data set was split into test and training sets using D-optimal experimental design to generate a diverse training set. Internal (R 2 cross-validated = 0.898 − 0.998) and external (R 2 cross-validated = 0.847 − 0.996) validation showed the models to be both stable and highly predictive. Enthalpies of formation were best described by electrotopological, atomic composition and molecular refractivity descriptors, while Kier and Hall χ and κ descriptors as well as the number of rotatable bonds appear frequently in models describing the entropy of formation of these compounds. Heat capacity models often feature the molecular area descriptor as well as the Kier and Hall 0χ descriptor and the number of methyl groups present in the molecule.

Acknowledgements

The authors wish to acknowledge the Dutch Polymer Institute (DPI), Project #500, for financial support of this work. Furthermore, Brian Pauw is gratefully acknowledged for help with MatLab programming.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 827.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.