Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

Abstract

Predicting plant cuticle-water partition coefficients (Kcw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental Kcw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict Kcw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logKcw values for 125 unique compounds were collected and curated. A poly-parameter linear free energy relationship (pp-LFER) model was developed with stepwise multiple linear regression based on this dataset. The resulted pp-LFER model has good predictability and robustness as indicated by determination coefficient (R2adj,tra) of 0.93, bootstrapping coefficient (Q2BOOT) of 0.92, external validation coefficient (Q2ext) of 0.94 and root mean square error of 0.52 log units. Contribution analysis of different interactions indicated that dispersion and hydrophobic interactions have the highest positive contribution (56%) to increase the partition of pollutants onto plant cuticles. In addition, for organic pollutions containing benzene ring (13–31%), double bond (9–17%) or nitrogen-containing heterocycles (9–17%), π/n-electron pairs interactions exhibit obvious positive contributions to logKcw. In conclusion, the proposed pp-LFER model is beneficial for predicting logKcw of potential organic pollutants directly from their molecular structures.