Predicting Partition Coefficients of Neutral Organics in Biotic and Abiotic Environmental Systems from Key Solvent Systems

Accurate prediction of equilibrium partition coefficients of organic compounds among biotic and abiotic environmentally relevant media is critical for evaluating their environmental fate and bioaccumulation potential, and for guiding drug design and toxicological assessment. Directly applying theoretical and data-driven approaches developed for solvents to these media is often challenging due to their complexity and limited experimental data. Linear solvation energy relationships (LSERs) using Abraham solute descriptors have achieved significant success in describing environmentally relevant partitioning systems. Yet, limited availability of high-quality experimental descriptors, particularly for structurally complex compounds, hinders their practical application and constrains the accuracy of descriptor prediction models. Here, we propose a four-parameter LSER (4SD-LSER) employing widely used logarithmic n-hexadecane–air, n-octanol–water and air–water partition coefficients and topological McGowan molar volume as solute descriptors. To assess performance, we compiled 1,836 experimental partition coefficients for 792 neutral compounds across 12 representative environmentally relevant systems. The 4SD-LSERs were calibrated and demonstrated good descriptive performance for these systems. Remarkably, when combined with appropriate fragment- or machine learning-based descriptor predictive models, the 4SD-LSER achieves prediction errors largely within ±0.5 log units for structurally simple compounds, and within ±1.0 log unit for more complex compounds (e.g., pesticides, pharmaceuticals, and flame retardants), reaching state-of-the-art accuracy. This work demonstrates that models developed for well-characterized solvent systems to predict partition coefficients or solvation free energies can be effectively extended to environmentally relevant systems via LSER.