2026-10-14T12:56:26Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33931537 2026-09-18T11:12:02Z category_1 category_272 category_873 category_39 category_734 category_61 category_69 portal_63 item_type_3 month_year_09_2026

Soil Sorption Estimator (SSE): Read-Across-Based Predictions of Soil Organic Carbon Partition Coefficient (Log K_oc) and Validation Using Multiple External Sets Shubham Kumar Pandey (25071805) Souvik Pore (23622580) Kunal Roy (536530) Biophysics Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified Developmental Biology Inorganic Chemistry soil sorption potential soil sorption estimator chemical regulatory bodies among various properties sse ): read soil organic carbon 2 </ sup comparison results depict based predictive platform tr </ sub test </ sub oc </ sub previously developed models new organic chemicals >< sup r </ quantitative read q </ predictive qualities organic chemicals k </ >< sub “< b untested chemicals providing insight present study models (< major concerns limited number labor required key parameter high cost efficient prediction chemicals due 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 (<i>K</i>_oc) 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 (<i>q</i>-RASPR) models for the fast and efficient prediction of Log <i>K</i>_OC values of new or untested chemicals. The statistical and predictive qualities of our models (<i>R</i>² > 0.84, <i>Q</i>_LOO² > 0.84, <i>Q</i>_F1² > 0.87, MAE_tr < 0.34, MAE_test < 0.44) depict their efficiency in predicting Log <i>K</i>_OC 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, “<b>Soil Sorption Estimator (SSE)</b>” has been developed to compute Log <i>K</i>_OC of chemicals with applicability domain (AD) information, providing insight into the reliability of predictions. 2026-09-18T00:00:00Z Dataset Dataset 10.1021/acs.est.6c06939.s001 https://figshare.com/articles/dataset/Soil_Sorption_Estimator_SSE_Read-Across-Based_Predictions_of_Soil_Organic_Carbon_Partition_Coefficient_Log_K_sub_oc_sub_and_Validation_Using_Multiple_External_Sets/33931537 CC BY-NC 4.0