Three linear regression methods were used to develop models for the prediction of rat tissue-air partition coefficient (P). In general, ridge regression (RR) was found to be superior to principal component regression (PCR) and partial least squares regression (PLS). A set of 46 diverse low molecular-weight volatile chemicals was used to model fat-air, liver-air and muscle-air partition coefficients for male Fischer 344 rats. Comparisons were made between models developed using descriptors based solely on molecular structure and those developed using experimental properties, including saline-air and olive oil-air partition coefficients, as independent variables, indicating that the structure-property correlations are comparable to the property-property correlations. Multiple structure-based models were developed utilizing various classes of structural descriptors based on level of complexity, i.e. topostructural (TS), topochemical (TC), 3-dimensional (3D) and calculated octanol-water partition coefficient. In most cases, the structure-based models developed using only the TC descriptors were found to be superior to those developed using other structural descriptor classes. Haloalkane subgroups were modeled separately for comparative purposes, and although models based on the congeneric compounds were superior, the models developed on the complete sets of diverse compounds were acceptable. Comparisons were also made with respect to the types of descriptors important for partitioning across the various media.