AI Summary

Researchers at Sejong University and the Korea Institute of Science and Technology (KIST) created explainable machine‑learning models that forecast key intestinal absorption properties of phytochemicals directly from their molecular structures. The models target bioactive plant compounds such as flavonoids, alkaloids and ginsenosides, which often fail to enter the bloodstream because they are poorly absorbed in the intestine. By predicting absorption without labor‑intensive cell‑based experiments, the approach aims to accelerate screening of functional‑food ingredients and drug candidates. The findings were published in the Journal of the Science of Food and Agriculture.

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