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Related Experiment Video

Updated: Aug 1, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from

Quang-Hien Kha1,2, Viet-Huan Le1,2,3, Truong Nguyen Khanh Hung4

  • 1International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

This study introduces a new AI model to predict drug-food interactions (DFIs), aiming to prevent adverse health effects from combining medications and food constituents. The model offers accurate recommendations to improve patient safety during therapy.

Keywords:
DrugBankFooDBadverse food reactionchemical informaticsdrug–food interactionsdrug–nutrient interactionsexplainable artificial intelligencemachine learningprecision medicinesimplified molecular-input line-entry system

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Area of Science:

  • Pharmacology and Computational Chemistry
  • Artificial Intelligence in Healthcare

Background:

  • Drug-food constituent interactions (DFIs) can alter therapeutic efficacy and patient health.
  • The prevalence of polypharmacy increases the risk of drug-drug interactions (DDIs) and DFIs, yet DFIs are often underestimated.
  • Existing AI models for DFI prediction face limitations in data mining, input processing, and annotation detail.

Purpose of the Study:

  • To develop a novel and accurate prediction model for drug-food constituent interactions (DFIs).
  • To address limitations in current data mining, input, and annotation methods for DFI studies.
  • To provide clinically relevant recommendations for avoiding adverse drug-food interactions.

Main Methods:

  • Extracted 70,477 food compounds from FooDB and 13,580 drugs from DrugBank.
  • Engineered 3,780 features for each drug-food compound pair.
  • Utilized and optimized the eXtreme Gradient Boosting (XGBoost) algorithm for DFI prediction.
  • Validated the model on an external dataset of 1,922 DFIs.

Main Results:

  • The XGBoost model demonstrated high accuracy in predicting drug-food interactions.
  • The model successfully predicted known DFIs and provided clinically relevant recommendations.
  • External validation confirmed the model's robust performance on unseen data.

Conclusions:

  • The proposed AI model effectively predicts drug-food interactions, enhancing patient safety.
  • This tool can assist healthcare professionals in guiding patients to avoid potentially severe adverse events from combined drug and food intake.
  • The model contributes to the development of more reliable predictive systems for managing drug-food interactions in clinical practice.