Drug repositioning, the discovery of new therapeutic uses for existing drugs, is increasingly gaining attention as a cost-effective and high-yield drug discovery strategy. Existing methods integrate diverse biological information into heterogeneous networks, providing a comprehensive framework for understanding complex drug–disease associations, which also will introduces noise into the data and affect the performance of the model. In this paper, first, a novel drug repositioning method, namely DVGEDR, is proposed, which generates two subgraphs of the target drug–disease pair to fuse biological information and integrate drug–disease associations from two distinct perspectives: drug–disease heterogeneous network and similarity networks. Next, a Multiple Attention Graph encoder (MAGencoder) module is designed to learn subgraph features and explore relationships between entities, which also improve the interpretability of the model. Finally, a graph enhancement mechanism is devised to improve the perception of critical information of model, enabling the model to flexibly process different graph structures. Performance comparisons with baseline models on three public datasets validate the state-of-the-art performance of DVGEDR in the field of drug repositioning. In case study, DVGEDR identifies 10 new candidate drugs for breast cancer and COVID-19, demonstrating not only superior performance in experimental settings but also potential therapeutic advantages in clinical environments. Furthermore, we select two sets of instances and further analyzed the attention distribution of the different nodes in the subgraph to explain the decision process of the model.

药物重新定位，即发现现有药物的新治疗用途，作为一种具有成本效益和高产量的药物发现策略，越来越受到关注。现有方法将不同的生物信息集成到异构网络中，为理解复杂的药物-疾病关联提供了一个全面的框架，这也会将噪声引入数据并影响模型的性能。本文首先提出了一种新的药物重定位方法，即 DVGEDR，该方法生成目标药物-疾病对的两个子图，以融合生物信息并从两个不同的角度整合药物-疾病关联：药物-疾病异质网络和相似性网络。接下来，设计了多注意力图编码器 （MAGencoder） 模块来学习子图特征并探索实体之间的关系，这也提高了模型的可解释性。最后，设计了一种图增强机制，以提高模型对关键信息的感知，使模型能够灵活地处理不同的图结构。与三个公共数据集上的基线模型进行性能比较，验证了 DVGEDR 在药物重新定位领域的最新性能。在案例研究中，DVGEDR 确定了 10 种针对乳腺癌和 COVID-19 的新候选药物，不仅在实验环境中表现出卓越的性能，而且在临床环境中也显示出潜在的治疗优势。此外，我们选择了两组实例，并进一步分析了子图中不同节点的注意力分布，以解释模型的决策过程。