Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI

• URL: http://arxiv.org/abs/2508.18766v1
• Date: Tue, 26 Aug 2025 07:50:47 GMT
• Title: Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
• Authors: Hongbo Liu, Siyi Li, Zheng Yu,
• Abstract summary: Drug-drug interactions (DDIs) can lead to reduced therapeutic efficacy or severe adverse effects.<n>We propose HGNN-DDI, a graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources.<n> Experimental results on benchmark DDI datasets demonstrate that HGNN-DDI outperforms state-of-the-art baselines in prediction accuracy and robustness.
• Score: 8.48967497162918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships among drugs, targets, and biological entities. In this work, we propose HGNN-DDI, a heterogeneous graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources. HGNN-DDI leverages graph representation learning to model heterogeneous biomedical networks, enabling effective information propagation across diverse node and edge types. Experimental results on benchmark DDI datasets demonstrate that HGNN-DDI outperforms state-of-the-art baselines in prediction accuracy and robustness, highlighting its potential to support safer drug development and precision medicine.

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