Extending Partition Coefficient Predictions from Key Solvent Systems to Biological and Environmental Systems via Linear Solvation Energy Relationship

Abstract

Accurate prediction of equilibrium partition coefficients of organic compounds in biological and environmental media is critical for evaluating their environmental fate and bioaccumulation potential, as well as for guiding drug design and toxicology studies. Linear solvation energy relationships using Abraham solute descriptors (ASD-LSERs) have achieved remarkable success in characterizing such complex biological and environmental partitioning systems. However, the limited availability of high-quality experimental descriptors, particularly for structurally complex compounds, hinders their practical application and further constrains the accuracy of descriptor predictive models. Here, we propose a linear solvation energy relationship (4SD-LSER) using descriptors derived from logarithmic n-hexadecane–air, n-octanol–air, and water–air partition coefficients, along with the topological McGowan molar volume. To evaluate its performance, 1,849 experimental partition coefficients for 779 neutral compounds across 12 biologically and environmentally relevant systems were compiled. The 4SD-LSER was calibrated and exhibited good descriptive power for these systems. Remarkably, when combined with appropriate fragment-based or machine learning-based descriptor prediction approaches, the 4SD-LSER achieved 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), exhibiting state-of-the-art accuracy, especially for complex compounds. This study demonstrates that models originally developed for well-characterized solvent systems to predict partition coefficients or solvation free energies can be readily extended to biological and environmental systems via LSER. Its performance is poised to improve further with advances in theoretical and machine-learning approaches.