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Phytochemicals are bioactive compounds derived from plants, such as flavonoids, alkaloids and ginsenosides. They are key ingredients in functional foods and promising starting points for new drugs. Yet many of them never make it into the bloodstream because they are poorly absorbed in the intestine. Measuring their absorption one compound at a time requires laborious cell-based experiments, and this bottleneck has long slowed the screening of candidate ingredients.

Researchers at Sejong University and the Korea Institute of Science and Technology (KIST) have now developed explainable machine learning models that predict key intestinal absorption properties of phytochemicals directly from their molecular structure, before any laboratory experiment is performed. The study was published in the Journal of the Science of Food and Agriculture.

The team focused on three indicators that are routinely measured in the human intestinal Caco-2 cell model, the standard laboratory system for studying intestinal absorption. Apparent permeability (Papp) reflects how readily a compound crosses the intestinal cell layer. Transepithelial electrical resistance (TEER) indicates the integrity of the intestinal barrier. The efflux ratio (ER) reveals whether transporter proteins actively pump a compound back out of the cells.

Starting from experimentally measured values for 83 structurally diverse phytochemicals, the researchers computed 5,003 molecular descriptors from each chemical structure. These descriptors are numerical features that encode the electronic, topological and structural characteristics of a compound.

A workflow combining statistical feature selection, recursive feature elimination and repeated cross-validation was used to compare 10 regression algorithms for each indicator.

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