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Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network

Nature Computational Science volume 3, pages 1023–1033 (2023)Cite this article

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Abstract

Drug–drug interactions (DDIs) for emerging drugs offer possibilities for treating and alleviating diseases, and accurately predicting these with computational methods can improve patient care and contribute to efficient drug development. However, many existing computational methods require large amounts of known DDI information, which is scarce for emerging drugs. Here we propose EmerGNN, a graph neural network that can effectively predict interactions for emerging drugs by leveraging the rich information in biomedical networks. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. The edges of the biomedical network are weighted to indicate the relevance for the target DDI prediction. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.

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Fig. 1: Overview of EmerGNN.
Fig. 2: Complexity analysis of different GNN-based methods in the S1 setting.
Fig. 3: Analysis of relation types on the selected paths on the DrugBank dataset.
Fig. 4: Visualization of drug pairs.
Fig. 5: Ablation studies on the DrugBank dataset.

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Data availability

The resplit dataset35 in DrugBank, TWOSIDES and HetioNet for the S1 and S2 settings is publicly available at https://doi.org/10.5281/zenodo.10016715. Source data are provided with this paper.

Code availability

The code for EmerGNN36 is available at https://github.com/LARS-research/EmerGNN.

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Acknowledgements

This project was supported by the National Natural Science Foundation of China (no. 92270106) and the CCF-Tencent Open Research Fund.

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Authors and Affiliations

  1. 4Paradigm Inc., Beijing, China

    Yongqi Zhang

  2. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Quanming Yao & Ling Yue

  3. Tencent Jarvis Lab, Shenzhen, China

    Xian Wu, Ziheng Zhang, Zhenxi Lin & Yefeng Zheng

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  1. Yongqi Zhang
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  2. Quanming Yao
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  3. Ling Yue
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  4. Xian Wu
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  5. Ziheng Zhang
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  6. Zhenxi Lin
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  7. Yefeng Zheng
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Contributions

Y. Zhang contributed to idea development, algorithm implementation, experimental design, results analysis and writing of the paper. Q.Y. contributed to idea development, experimental design, results analysis and writing of the paper. L.Y. contributed to algorithm implementation and results analysis. Y. Zheng contributed to results analysis and writing of the paper. All authors read, edited and approved the paper.

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Correspondence to Quanming Yao.

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Nature Computational Science thanks Nguyen Quoc Khanh Le, Jian-Yu Shi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Zhang, Y., Yao, Q., Yue, L. et al. Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network. Nat Comput Sci 3, 1023–1033 (2023). https://doi.org/10.1038/s43588-023-00558-4

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