Predicting drug–disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug–disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug–disease association prediction model based on graph convolutional network and graph attention network (GCNGAT) to reposition marketed drugs under the distinguishment of background information. Firstly, in order to obtain initial drug–disease information, a drug–disease heterogeneous graph structure is constructed based on all known drug–disease associations. Secondly, based on the heterogeneous graph structure, the corresponding subgraphs of each group of drug–disease association pairs are extracted to distinguish different background information for the same drug from different diseases. Finally, a model combining Graph neural network with global Average pooling (GnnAp) is designed to predict potential drug–disease associations by learning drug–disease interaction feature representations. The experimental results show that adding subgraph extraction can effectively improve the prediction performance of the model, and the graph representation learning module can fully extract the deep features of drug–disease. Using the 5-fold cross-validation, the proposed model (GCNGAT) achieves AUC (Area Under the receiver operating characteristic Curve) values of 0.9182 and 0.9417 on the PREDICT dataset and CDataset dataset, respectively. Compared with other predictors on the same dataset (PREDICT dataset), GCNGAT outperforms the existing best-performing model (PSGCN), with a 1.58% increase in the AUC value. It is anticipated that this model can provide experimental reference for drug repositioning and further promote the drug research and development process.

中文翻译：

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GCNGAT：基于图卷积神经网络和图注意力网络的药物-疾病关联预测

预测药物与疾病的关联有助于发现药物新的治疗潜力，为新药研发提供重要的关联信息。现有的许多药物与疾病关联预测方法并没有区分同一种药物针对不同疾病的相关背景信息。因此，本文提出一种基于图卷积网络和图注意网络（GCNGAT）的药物-疾病关联预测模型，在背景信息的区分下对已上市药物进行重新定位。首先，为了获得初始的药物-疾病信息，基于所有已知的药物-疾病关联构建药物-疾病异构图结构。其次，基于异构图结构，提取每组药物-疾病关联对对应的子图，以区分同一药物在不同疾病中的不同背景信息。最后，设计了一个将图神经网络与全局平均池化（GnnAp）相结合的模型，通过学习药物-疾病相互作用特征表示来预测潜在的药物-疾病关联。实验结果表明，加入子图提取可以有效提高模型的预测性能，图表示学习模块可以充分提取药物-疾病的深层特征。使用 5 折交叉验证，所提出的模型 (GCNGAT) 在 PREDICT 数据集和 CDataset 数据集上分别实现了 0.9182 和 0.9417 的 AUC（接收者工作特征曲线下面积）值。与同一数据集（PREDICT数据集）上的其他预测器相比，GCNGAT优于现有的最佳性能模型（PSGCN），AUC值增加了1.58%。期待该模型能为药物重新定位提供实验参考，进一步推动药物研发进程。