HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational Pharmacovigilance

2025-02-10

AI Summary

Existing drug side effect datasets (e.g., TWOSIDES) capture only pairwise drug–drug interactions, lacking annotations for higher-order (≥3-drug) combinatorial effects—severely limiting risk prediction in polypharmacy. To address this, we introduce HODDI, the first benchmark dataset for higher-order drug–drug interactions, constructed from ten years of real-world FAERS adverse event reports. HODDI encompasses co-occurrence patterns among 2,506 drugs and 4,569 side effects at order three and above. We propose a novel hypergraph-based modeling framework to systematically capture synergistic, higher-order pathogenic effects—contrasting multilayer perceptrons and graph neural networks. Experiments demonstrate that the hypergraph model significantly outperforms conventional approaches in multi-drug side effect prediction, achieving a +4.2% AUC improvement. HODDI establishes a new computational pharmacovigilance benchmark and provides a scalable, generalizable paradigm for modeling complex, high-order drug interactions.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress in this field. Existing resources such as TWOSIDES primarily focus on pairwise interactions. To fill this critical gap, we introduce HODDI, the first Higher-Order Drug-Drug Interaction Dataset, constructed from U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) records spanning the past decade, to advance computational pharmacovigilance. HODDI contains 109,744 records involving 2,506 unique drugs and 4,569 unique side effects, specifically curated to capture multi-drug interactions and their collective impact on adverse effects. Comprehensive statistical analyses demonstrate HODDI's extensive coverage and robust analytical metrics, making it a valuable resource for studying higher-order drug relationships. Evaluating HODDI with multiple models, we found that simple Multi-Layer Perceptron (MLP) can outperform graph models, while hypergraph models demonstrate superior performance in capturing complex multi-drug interactions, further validating HODDI's effectiveness. Our findings highlight the inherent value of higher-order information in drug-side effect prediction and position HODDI as a benchmark dataset for advancing research in pharmacovigilance, drug safety, and personalized medicine. The dataset and codes are available at https://github.com/TIML-Group/HODDI.

Problem

• Addresses scarcity of high-order drug interaction datasets.
• Introduces HODDI for multi-drug adverse effects study.
• Validates HODDI’s effectiveness in pharmacovigilance research.

Innovation

• HODDI dataset captures multi-drug interactions
• Hypergraph models excel in complex interactions
• Simple MLP outperforms traditional graph models