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Updated: May 9, 2026
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Molecular descriptor driven QSPR modeling of Papp, TEER and Efflux Ratio from Caco-2 cells using machine learning for
Jin-Woo Kim1, Rixing Cong1, Jin-Soo Park2,3
1Department of Food Science and Biotechnology, Sejong University, Seoul, Republic of Korea.
Background:
The present study aimed to develop and validate quantitative structure-property relationship (QSPR) models for predicting permeability related bioavailability indicators including apparent permeability (Papp), trans-epithelial electrical resistance (TEER) and efflux ratio (ER) based on molecular descriptors (n = 5003) of 83 phytochemicals. A rigorous workflow integrating feature selection and 10 regression algorithms were employed to assess predictive performance.
Results:
Pearson correlation coefficient analysis revealed significant relationships among the three indictors. Among the models, a stacking ensemble, including CatBoost, LightGBM and Gradient Boosting as base learners and linear regression as the meta-model, achieved strong predictive accuracy for Papp and ER, at the same time as showing moderate but highly variable performance for TEER. SHapley Additive exPlanations (i.e. SHAP)-based feature importance analysis provided insight into key molecular descriptors associated with electronic, topological and branching.
Conclusion:
These results demonstrate the utility of interpretability tools in constructing robust and explainable QSPR models for bioavailability prediction in various phytochemicals. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
