A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

• URL: http://arxiv.org/abs/2411.00801v1
• Date: Sun, 20 Oct 2024 14:57:24 GMT
• Title: A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction
• Authors: Junwei Hu, Michael Bewong, Selasi Kwashie, Wen Zhang, Vincent M. Nofong, Guangsheng Wu, Zaiwen Feng,
• Abstract summary: Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets.
• Score: 3.1923357295923225
• License:
• Abstract: Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://github.com/Zaiwen/HNCL-DTI.

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