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

TL;DR

HODDI is the first comprehensive dataset capturing higher-order drug-drug interactions from FDA reports, enabling improved computational analysis of complex multi-drug adverse effects.

Contribution

This paper introduces HODDI, a novel dataset for higher-order drug interactions, filling a critical gap beyond pairwise data and facilitating advanced pharmacovigilance research.

Findings

Hypergraph models outperform simpler models in capturing complex interactions.

Simple MLP can outperform graph models in some scenarios.

HODDI provides extensive coverage of multi-drug adverse effects.

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.