Statistical equivalence of prediction models of the soil sorption coefficient obtained using different log P algorithms

Abstract

The soil sorption coefficient normalized to the organic carbon content (K_oc) is a physicochemical parameter used in environmental risk assessments and in determining the final fate of chemicals released into the environment. Several models for predicting this parameter have been proposed based on the relationship between log K_oc and log P. The difficulty and cost of obtaining experimental log P values led to the development of algorithms to calculate these values, some of which are free to use. However, quantitative structure-property relationship (QSPR) studies did not detail how or why a particular algorithm was chosen. In this study, we evaluated several free algorithms for calculating log P in the modeling of log K_oc, using a broad and diverse set of compounds (n = 639) that included several chemical classes. In addition, we propose the adoption of a simple test to verify if there is statistical equivalence between models obtained using different data sets. Our results showed that the ALOGPs, KOWWIN and XLOGP3 algorithms generated the best models for modeling K_oc, and these models are statistically equivalent. This finding shows that it is possible to use the different algorithms without compromising statistical quality and predictive capacity.

Keywords: Environmental risk; Modeling; QSPR; Soil contamination.