Predicting Storage–Lipid Water Partitioning of Organic Solutes from Molecular Structure

Partitioning to storage fat is the major process for bioaccumulation of many neutral organic chemicals. In this work, we evaluated the performance of four predictive models, ABSOLV, COSMOtherm, KOWWIN, and SPARC to calculate storage lipid–water partition coefficients. In a first step of the validation, we used over 300 literature data for chemicals with relatively simple molecular structures. For these compounds the overall performance was similar for all models with a root-mean-square error (rmse) between 0.45 and 0.61 log units. Clear differences became visible in the second validation step where a subset with only H-bond-donor compounds was used. Here, COSMOtherm and SPARC performed clearly better with an rmse of 0.35 and 0.42 log units, respectively, compared to ABSOLV and KOWWIN with an rmse of 0.91 and 0.85 log units, respectively. The last step in our validation was a comparison with experimental values for 22 complex, multifunctional chemicals (including pesticides, hormones, mycotoxins) that we measured specifically for this validation purpose. For these chemicals, predictions by all models were less accurate than those for simpler chemicals. COSMOtherm performed the best (rmse 0.71 log units) while the other methods showed considerably poorer results (rmse 1.29 (ABSOLV), 1.25 (SPARC), and 1.62 (KOWWIN) log units).