K _OC-WebPredictor: An Open-Access Tool for Prediction and Insights into Soil Sorption

The soil organic carbon-water partition coefficient (K _OC) is a key determinant of the environmental mobility and persistence of organic contaminants. Experimental measurement of K _OC is accurate but resource-intensive, limiting its availability for the vast chemical inventory in commerce. Here, we developed interpretable quantitative structure-activity relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) models, along with machine learning (ML) approaches, to predict log K _OC values using reproducible 1D and 2D molecular descriptors. The optimized multiple linear regression (MLR)-based QSAR model, built on 824 structurally diverse compounds and nine mechanistically relevant descriptors, achieved strong internal and external performance (R ² = 0.85, Q ² _LOO = 0.84, and Q ² _F1 = 0.84). Comparative statistical evaluation using paired t- and Wilcoxon signed-rank tests confirmed that the QSAR model significantly outperformed the q-RASAR variant (p < 0.05) in predictive accuracy and robustness. Mechanistic interpretation revealed that hydrophobicity, aromatic rigidity, and halogenation increase soil sorption, whereas polar or phosphorus-rich substituents promote mobility. Large-scale external screening of 7,612 chemicals from the U.S. EPA's CPDat inventory showed 94% coverage within the applicability domain (AD), supporting data gap filling under regulatory frameworks. An open-access web tool, K _OC-WebPredictor, was developed to deliver quantitative (QSAR-based) and qualitative (ML-based) predictions, with visualization taking AD into consideration. This integrated, interpretable platform provides a practical alternative to experimental assays for assessing soil-organic carbon interactions and prioritizing chemicals based on mobility potential.