Chemical Properties-Based Deep Learning Models for Recommending Rational Daily Diet Combinations to Diabetics Through Large-Scale Virtual Screening of α-Glucosidase Dietary-Derived Inhibitors and Verified In Vitro

The lack of suitable chemical research methodologies has hindered the discovery of rational daily diet combinations from large-scale dietary-derived compounds. Three deep learning models based on chemical properties for α-glucosidase inhibitors (AGIs), safety, and drug-drug interaction (DDI) were trained. The trained models screened potential AGIs from the FooDB database (approximately 70,000 food-derived compounds) and analyzed the interactions of the selected AGIs. 59 of the 75 selected AGIs from the FooDB database had not been reported before. Betulinic acid in combination with taraxasterol, betulin, and lupeol (all selected from the potential 75 AGIs) was predicted to have a synergistic effect in enhancing the inhibition of α-glucosidase, which was further confirmed by in vitro assays. These collective findings strongly suggest that the potential of deep learning methods based on chemical properties in solving the food chemistry research challenge of developing reasonable daily diet combinations.