通过对比学习提升药物靶点相互作用预测精度
A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction
- 构建异质图注意力网络，同时关注节点和边的特征
- 在多个基准数据集上超越现有先进方法
- 适合药物研发与再定位领域的研究人员
药物-靶点相互作用（DTI）预测对药物研发与再定位至关重要。基于异质图神经网络（HGNN）的方法已成为主流，其中基于注意力的模型表现优异。然而，这些方法通常忽略异质生物医学网络中的边特征。本文提出一种基于异质网络的对比学习方法HNCL-DTI，设计异质图注意力网络以预测潜在或新型药物-靶点相互作用。具体而言，该方法利用对比学习，从节点和边两个角度协同学习异质结构中的节点表示。实验结果表明，HNCL-DTI在多个基准数据集上优于现有先进基线方法，展现出强大的预测能力与实际有效性。代码与数据已公开于https://github.com/Zaiwen/HNCL-DTI。
原文摘要 · Abstract (English)
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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