Predicting blood:air partition coefficients using theoretical molecular descriptors

Subhash C Basak, Denise Mills, Hisham A. El-Masri, Moiz M. Mumtaz, Douglas M Hawkins

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Three regression methods, namely ridge regression (RR), partial least squares (PLS), and principal components regression (PCR), were used to develop models for the prediction of rat blood:air partition coefficient for increasingly diverse data sets. Initially, modeling was performed for a set of 13 chlorocarbons. To this set, 10 additional hydrophobic compounds were added, including aromatic and non-aromatic hydrocarbons. A set of 16 hydrophilic compounds was also modeled separately. Finally, all 39 compounds were combined into one data set for which comprehensive models were developed. A large set of diverse, theoretical molecular descriptors was calculated for use in the current study. The topostructural (TS), topochemical (TC), and geometrical or 3-dimensional (3D) indices were used hierarchically in model development. In addition, single-class models were developed using the TS, TC, and 3D descriptors. In most cases, RR outperformed PLS and PCR, and the models developed using TC indices were superior to those developed using other classes of descriptors.

Original languageEnglish (US)
Pages (from-to)45-55
Number of pages11
JournalEnvironmental Toxicology and Pharmacology
Issue number1-2
StatePublished - Mar 2004

Bibliographical note

Funding Information:
This is contribution number 347 from the Center for Water and the Environment of the Natural Resources Research Institute. Research reported in this paper was supported in part by Grant F49620-02-1-0138 from the United States Air Force and Cooperative Agreement Number 572112 from the Agency for Toxic Substances and Disease Registry. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of ATSDR.

Copyright 2008 Elsevier B.V., All rights reserved.


  • Physiologically based pharmacokinetic models
  • Quantitative structure-activity relationships
  • Ridge regression
  • Risk assessment
  • Volatile organic chemicals (VOCs)


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