Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915464223916032 |
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| author | Liu, Hongbo Li, Siyi Yu, Zheng |
| author_facet | Liu, Hongbo Li, Siyi Yu, Zheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI Liu, Hongbo Li, Siyi Yu, Zheng Machine Learning Primary 68T07, Secondary 68T05, 68R10, 92C50 I.2.6; I.5.1; H.2.8; J.3 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. |
| title | Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI |
| topic | Machine Learning Primary 68T07, Secondary 68T05, 68R10, 92C50 I.2.6; I.5.1; H.2.8; J.3 |
| url | https://arxiv.org/abs/2508.18766 |