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

Predicting plant cuticle-water partition coefficients (K_cw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental K_cw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict K_cw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logK_cw 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 (R² _adj,tra) of 0.93, bootstrapping coefficient (Q² _BOOT) of 0.92, external validation coefficient (Q² _ext) 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 logK_cw. In conclusion, the proposed pp-LFER model is beneficial for predicting logK_cw of potential organic pollutants directly from their molecular structures.