Drug repositioning (DR) is a promising approach for identifying novel indications of existing drugs. Computational methods for drug repositioning have been recognised as effective ways to discover the associations between drugs and diseases. However, most computational DR methods ignore the significance of heterogeneous graph augmentation when conducting contrastive learning, which plays a critical role in improving the generalisation and robustness. The high-order similarity information from multiple data sources is still under-explored. Furthermore, only a limited number of computational DR methods can effectively screen for the most informative negative samples for model training. To address these limitations, we propose a novel DR method called DRMAHGC that employs multi-aspect graph contrastive learning to predict drug-disease associations (DDAs). First, high-order features were generated from the similarity network using a graph-masked autoencoder. Then, heterogeneous graph contrastive learning with structure- and metapath-level augmentation was employed to enhance semantic comprehension and learn expressive representations. Subsequently, the positive-fusion negative sampling strategy was exploited to synthesise informative negative sample embeddings to train the classifier for predicting novel DDAs. Extensive results on three benchmark datasets indicate that DRMAHGC significantly and consistently outperformed the state-of-the-art methods in the DR task. Moreover, the case study of two common diseases further demonstrates its effectiveness and provides novel insights into DRMAHGC in identifying novel DDAs.

中文翻译：

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通过多方面异构图对比学习和正融合负采样策略进行药物重新定位


药物重新定位（DR）是识别现有药物新适应症的一种有前途的方法。药物重新定位的计算方法已被认为是发现药物与疾病之间关联的有效方法。然而，大多数计算DR方法在进行对比学习时忽略了异构图增强的重要性，而异构图增强对于提高泛化性和鲁棒性起着至关重要的作用。来自多个数据源的高阶相似性信息仍有待探索。此外，只有有限数量的计算DR方法可以有效地筛选出最具信息量的负样本来进行模型训练。为了解决这些限制，我们提出了一种称为 DRMAHGC 的新型 DR 方法，该方法采用多方面图对比学习来预测药物与疾病关联 (DDA)。首先，使用图掩码自动编码器从相似性网络生成高阶特征。然后，采用具有结构和元路径级别增强的异构图对比学习来增强语义理解并学习表达表示。随后，利用正融合负采样策略来合成信息丰富的负样本嵌入，以训练分类器来预测新的 DDA。三个基准数据集的广泛结果表明，DRMAHGC 在 DR 任务中始终显着优于最先进的方法。此外，两种常见疾病的案例研究进一步证明了其有效性，并为 DRMAHGC 识别新型 DDA 提供了新的见解。