Identification of new drug–target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug–protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, ‘graph convolutional network (GCN)-DTI’, for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

中文翻译：

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基于图卷积网络和深度神经网络识别药物-靶点相互作用

鉴定新的药物靶点相互作用 (DTI) 是药物发现中一个重要但耗时且成本高的步骤。近年来，为了减轻这些缺点，研究人员试图使用计算方法来识别 DTI。然而，大多数现有方法分别构建药物网络和目标网络，然后根据药物和目标之间的已知关联预测新的 DTI，而没有考虑药物-蛋白质对 (DPP) 之间的关联。为了将 DPP 之间的关联纳入 DTI 建模，我们构建了一个基于多种药物和蛋白质的 DPP 网络，其中 DPP 是节点，DPP 之间的关联是网络的边缘。然后，我们提出了一种新的基于学习的框架“图卷积网络 (GCN)-DTI”，用于 DTI 识别。该模型首先使用图卷积网络来学习每个 DPP 的特征。其次，使用特征表示作为输入，它使用深度神经网络来预测最终标签。我们的分析结果表明，所提出的框架在很大程度上优于一些最先进的方法。