research-article

Heterogeneous Graph Attention Network for Drug-Target Interaction Prediction

Authors:

Hua JiAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 1166 - 1176

https://doi.org/10.1145/3511808.3557346

Published: 17 October 2022 Publication History

Abstract

Identification of drug-target interactions (DTIs) is crucial for drug discovery and drug repositioning. Existing graph neural network (GNN) based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network, and are incapable of capturing long-range dependencies in the biological heterogeneous graph. In this paper, we propose the heterogeneous graph attention network (HGAN) to capture the complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. HGAN enhances heterogeneous graph structure learning from both the intra-layer perspective and the inter-layer perspective. Concretely, we develop an enhanced graph attention diffusion layer (EGADL), which efficiently builds connections between node pairs that may not be directly connected, enabling information passing from important nodes multiple hops away. By stacking multiple EGADLs, we further enlarge the receptive field from the inter-layer perspective. HGAN advances 15 state-of-the-art methods on two heterogeneous biological datasets, achieving the results near to 1 in terms of AUC and AUPR. We also find that enlarging receptive fields from the inter-layer perspective (stacking layers) is more effective than that from the intra-layer perspective (attention diffusion) for HGAN to achieve promising DTI prediction performances. The code is available at https://github.com/Zora-LM/HGAN-DTI.

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Cited By

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https://doi.org/10.3390/molecules29112483
Zhang BNiu DZhang LZhang QLi Z(2024)MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction predictionBMC Bioinformatics10.1186/s12859-024-05904-525:1Online publication date: 23-Aug-2024
https://doi.org/10.1186/s12859-024-05904-5
Li YChen XZhao YShan WWang ZYang GWang G(2024)Self-Training GNN-based Community Search in Large Attributed Heterogeneous Information Networks2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00216(2765-2778)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00216
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Index Terms

Heterogeneous Graph Attention Network for Drug-Target Interaction Prediction
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Hu BYu ZLi M(2024)MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction PredictionMolecules10.3390/molecules2911248329:11(2483)Online publication date: 24-May-2024
https://doi.org/10.3390/molecules29112483
Zhang BNiu DZhang LZhang QLi Z(2024)MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction predictionBMC Bioinformatics10.1186/s12859-024-05904-525:1Online publication date: 23-Aug-2024
https://doi.org/10.1186/s12859-024-05904-5
Li YChen XZhao YShan WWang ZYang GWang G(2024)Self-Training GNN-based Community Search in Large Attributed Heterogeneous Information Networks2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00216(2765-2778)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00216
Zhou YMa YLiu DShang JWang W(2024)GSDPI: An Integrated Feature Extraction Framework for Predicting Novel Drug-Protein InteractionAdvanced Intelligent Computing in Bioinformatics10.1007/978-981-97-5692-6_15(164-176)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-981-97-5692-6_15
Lusito E(2024)Deep Learning for Network BiologyBig Data Analysis and Artificial Intelligence for Medical Sciences10.1002/9781119846567.ch5(97-113)Online publication date: 24-May-2024
https://doi.org/10.1002/9781119846567.ch5
Liu LZhang QWei YZhao QLiao B(2023)A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug–Target Interaction PredictionMolecules10.3390/molecules2818654628:18(6546)Online publication date: 9-Sep-2023
https://doi.org/10.3390/molecules28186546
Zhao ZHe CQu YZheng HDuan LZuo J(2023)MGDTI: Graph Transformer with Meta-Learning for Drug-Target Interaction Prediction2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385671(801-806)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385671

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