Soil Sorption Estimator (SSE): Read-Across-Based Predictions of Soil Organic Carbon Partition Coefficient (Log Koc)...

The persistence and bioaccumulation of organic chemicals are major concerns for environmental and chemical regulatory bodies. Among various properties, the soil organic carbon-water partition coefficient (Koc) is a key parameter that governs the environmental fate of these organic chemicals. Experimental values of soil sorption potential are available for only a limited number of chemicals due to the high cost and labor required for experiments. In the present study, we have developed both quantitative structure-property relationship (QSPR) and quantitative read-across structure-property relationship (q-RASPR) models for the fast and efficient prediction of Log KOC values of new or untested chemicals. The statistical and predictive qualities of our models (R2 > 0.84, QLOO2 > 0.84, QF12 > 0.87, MAEtr < 0.34, MAEtest < 0.44) depict their efficiency in predicting Log KOC values for new organic chemicals. The developed models were validated on 5 external data sets, and their quality was also compared with that of previously developed models. The comparison results depict the superiority of our developed models, showing low prediction errors. A web-based predictive platform, "Soil Sorption Estimator (SSE)" has been developed to compute Log KOC of chemicals with applicability domain (AD) information, providing insight into the reliability of predictions.