Predictive QSAR models for rat and human tissue:air partition coefficients, namely blood:air, fat:air, brain:air, liver:air, muscle:air, and kidney:air were developed utilizing experimentally determined partition coefficients for 131 chemicals obtained from the literature and molecular descriptors based solely on chemical structure. The descriptors were partitioned into four hierarchical classes, including topostructural, topochemical, 3-dimensional, and ab initio quantum chemical. Three types of regression methodologies - ridge regression, principal components regression, and partial least squares regression - were used comparatively in the development of the structure-based models. In addition to the structure-based models, ordinary least squares regression was used to develop comparative models based on experimentally determined properties including saline:air and olive oil:air partition coefficients. The results of the study indicate that many of the structure-based models are comparable or superior to their respective property-based models. This is an important result considering that structural descriptors can be calculated quickly and inexpensively for both existing chemicals and those not yet synthesized. It was also found that ridge regression outperformed principal components regression and partial least squares regression, with respect to the structure-based models, and that generally the topochemical descriptors alone produced models of good predictive ability.
- Hierarchical quantitative structure-activity relationship (HiQSAR)
- Ridge regression
- Tissue:air partition coefficient
- Topological indices
- Volatile organic chemicals (VOCs)