Prediction of Human Blood: Air Partition Coefficient: A Comparison of Structure-Based and Property-Based Methods
Abstract
In recent years, there has been increased interest in the development and use of quantitative structure activity/property relationship (QSAR/QSPR) models. For the most part, this is due to the fact that experimental data is sparse and obtaining such data is costly, while theoretical structural descriptors can be obtained quickly and inexpensively. In this study, three linear regression methods, viz, principal component regression (PCR), partial least squares (PLS), and ridge regression (RR) were used to develop QSPR models for the estimation of human blood; air partition coefficient (logP blood;air) for a group of 31 diverse low-molecular weight volatile chemicals from their computed molecular descriptors. In general, RR was found to be superior to PCR or PLS. Comparisons were made between models developed using parameters based solely on molecular structure and linear regression (LR) models developed using experimental properties, including saline;air partition coefficient (longP saline;air) and olive oil;air partition coefficient (logP olive oil;air), as independent variables, indicating that the structure-property correlations are comparable to the property-property correlations. The best models, however, were those which used rat logP blooda;air as the independent variable. Haloalkane subgroups were modeled separately for comparative purposes, and although models based on the congeneric compounds were superior, the models developed on the complete set of diverse compounds were of acceptable quality. The structural descriptors were superior, the models developed on the complete set of diverse compounds were of acceptable quality.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 15, 2002
- Accession Number
- ADA408827
Entities
People
- D. M. Hawkins
- D. Mills
- H. A. El-masri
- Subhash C. Basak
Organizations
- University of Minnesota Duluth