ArticleScientific reports2022
A novel graph mining approach to predict and evaluate food-drug interactions.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- KG-DFI: A Prediction of Drug-Food Interactions Based on Knowledge Graph Embedding.Computational and structural biotechnology journal · 2026Article
- Decoding psychotropic-induced metabolic disturbances: gut-brain axis, multi-omics, and nutrition-lifestyle integration.Frontiers in nutrition · 2026Review
- ArcDFI: Attention regularization guided by CYP450 interactions for predicting drug-food interactions.PLoS computational biology · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach.BMC plant biology · 2025Article
- Predicting Natural Product-Drug Interactions with Knowledge Graph Embeddings.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Health effects of intermittent fasting and the role of artificial intelligence technologies in optimizing its clinical translation.AIMS public health · 2025Review
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Evaluating Pharmacists' Knowledge of Food-Drug Interactions in Croatia: Identifying Gaps and Opportunities.Pharmacy (Basel, Switzerland) · 2024Article
- DDID: a comprehensive resource for visualization and analysis of diet-drug interactions.Briefings in bioinformatics · 2024Article
- Enriching the FIDEO ontology with food-drug interactions from online knowledge sources.Journal of biomedical semantics · 2024Article
- Review
- Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures.Sensors (Basel, Switzerland) · 2023Article
- Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions.Journal of biomedical informatics · 2023Article
- Review
- Polypharmacy and precision medicine.Cambridge prisms. Precision medicine · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.
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Registered trials
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