Mapping the AI Frontier in Drug Interaction Prediction: Building Blocks for Egypt HealthGPA

Introduction: Why drug interactions matter

Every year, thousands of patients worldwide suffer harm because of unexpected drug interactions. These can occur between two drugs, between a drug and a disease, or even between drugs and the foods or supplements people consume. Some interactions are mild, but others can be life-threatening—leading to hospitalizations, treatment failures, or even fatalities.

Traditional laboratory and clinical approaches to detect interactions are often slow, costly, and limited in scope. As medicine becomes increasingly personalized, there’s an urgent need for faster, scalable tools that can predict risky interactions before they occur.

This is where artificial intelligence (AI) comes in. With the explosion of biomedical data—from drug databases to electronic health records—AI provides the analytical power to uncover hidden patterns, anticipate interactions, and suggest safer treatment pathways.

Our team at Nile University’s Centre for Informatics Science, as part of the Egypt HealthGPA project, set out to systematically map this emerging field. We wanted to answer a foundational question: how far has AI progressed in predicting the broad spectrum of drug interactions, and where are the gaps that remain?

What we did: The first comprehensive AI landscape for drug interactions

We performed a systematic review of 147 studies published between 2018 and 2024 that used AI to predict drug interactions of multiple kinds:

• Drug–drug interactions (DDIs): when one drug affects another’s absorption, metabolism, or effect.

• Drug–disease interactions (DDSIs): when a drug worsens or alters the course of an existing condition.

• Drug–nutrient interactions (DNIs): when foods, supplements, or the gut microbiome interfere with drugs.

• Drug–allergy interactions (DAIs): when a drug triggers harmful immune responses in predisposed patients.

To ensure rigor, we followed the PRISMA guidelines for systematic reviews, carefully screening thousands of papers and categorizing models, datasets, and outcomes.

Our goal was not only to summarize the state of the art but also to build a taxonomy of methods and resources—a kind of “map of the AI landscape” in drug interaction prediction.

What we found: A field in rapid growth, but with critical blind spots

1. Drug–drug interactions dominate the field
   Over half of the studies focused on DDIs, reflecting both the availability of data and the seriousness of these interactions. Here, graph-based models (like Graph Neural Networks) and deep learning methods have become increasingly popular, outperforming older machine learning approaches in accuracy.

2. Deep learning adds depth, but at a cost
   While deep learning excels at capturing complex patterns, it also suffers from problems like data scarcity, overfitting, and limited interpretability. Researchers are beginning to integrate knowledge graphs and explainable AI tools to make models more trustworthy for clinical use.

3. Other interaction types are underexplored
   Drug–food, drug–supplement, and drug–allergy interactions remain surprisingly underrepresented, even though they are clinically important. Similarly, drug–microbiome interactions—where gut bacteria activate or deactivate drugs—are an emerging but still sparse research area.

4. The rise of large language models (LLMs)
   Recent studies are beginning to apply LLMs like BioBERT and GPT-style models to process biomedical literature and unstructured clinical notes. By combining these with structured knowledge graphs, researchers are bridging data gaps and creating more robust, interpretable systems.

5. Challenges remain

• Data imbalance: Most datasets capture positive interactions but not well-documented negatives.

• Noise and heterogeneity: Clinical records, literature, and pharmacological data often conflict.

• Explainability: Clinicians need transparent models they can trust, not black boxes.

• Underrepresentation: Nutrient- and allergy-related interactions lack strong datasets.

Why this matters: Laying the foundation for Egypt HealthGPA

This review is more than an academic exercise. It represents the first building block of the Egypt HealthGPA project, which aims to create a national AI-powered drug interaction checker tailored to local and global healthcare needs.

By mapping the landscape, we identified which approaches are most promising, which datasets are most reliable, and where new innovation is needed. This gives us a blueprint for developing a GPA-powered clinical tool that can:

• Warn doctors and pharmacists about potential dangerous drug interactions in real time.

• Incorporate nutritional and disease context, not just drug–drug interactions.

• Support personalized medicine by integrating genomics, microbiome, and lifestyle data.

• Use explainable AI so that predictions can be trusted and acted upon in clinical practice.

The road ahead: From review to implementation

Our next steps are clear:

1. Building integrated datasets – pulling together drug, nutrient, disease, and microbiome data to reflect the true complexity of interactions.

2. Developing multimodal AI models – combining graph-based reasoning with LLMs for a hybrid system that is both powerful and interpretable.

3. Focusing on underexplored interactions – especially drug–nutrient and drug–allergy cases, which are highly relevant for everyday healthcare.

4. Deploying efficient, scalable models – exploring Small Language Models (SLMs) for real-time, privacy-preserving predictions that can work even in resource-limited healthcare settings.

Conclusion: Toward safer and smarter healthcare

Drug interactions are one of the most preventable causes of harm in medicine—but only if we can anticipate them. AI offers the tools to make this possible, and our review lays the groundwork for moving from fragmented research to integrated, clinical-grade solutions.

Through Egypt HealthGPA, we envision a future where an AI-powered checker sits at the heart of prescribing workflows—alerting clinicians, empowering patients, and making healthcare safer, more efficient, and more personalized.

Our systematic review is the first step in this journey: a map of where AI in drug interaction prediction stands today, and a compass pointing to the future.