Prediction of tissue-air partition coefficients: A comparison of structure-based and property-based methods

Abstract

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.

Prediction of h5-HT2A receptor antagonistic activity of arylindoles: Computational approach using topochemical descriptors

“…The topostructural and topochemical descriptors are collectively referred to as topological descriptors [13]. Topostructural descriptors encode information strictly on the adjacency and connectedness of atoms within a molecule whereas topochemical descriptors encode information relating to both molecular topology and the chemical nature of atoms and bonds within a molecule [14].…”

Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

“…Many articles have utilized ridge regression for dealing with the large-p, small-n problem in the QSAR literature [24,27,17,28,29]. In particular, [30] used ridge regression to determine whether biodescriptors provide additional information over chemodescriptors in predicting eight toxicity measures in 14…”

Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

“…Predictive models based on non-experimental molecular descriptors can provide cost effective and rapid estimates of partitioning behavior of contaminants. Topological, geometrical and quantum chemical indexes comprise a set of descriptors, which were useful in the prediction of properties of structurally similar molecules [27][28][29][30]. Basak and Mills [31] developed predictive models solely on topological and geometrical descriptors for S, K OW and K OC of 136 chemicals including 19 PAHs.…”