ArticleJournal of the science of food and agriculture2026
Molecular descriptor driven QSPR modeling of Papp, TEER and Efflux Ratio from Caco-2 cells using machine learning for various phytochemicals.
Article in Journal of the science of food and agriculture, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe 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.
resultsPearson 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.
conclusionThese 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.
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