Introduction: Identification of drug-target interactions (DTI) is a crucial step in drug development with high specificity and low toxicity. To accelerate the process, computer-aided DTI prediction algorithms have been used to screen compounds or targets rapidly. Furthermore, DTI prediction can be used to identify potential targets for existing drugs, thus uncovering new indications and repositioning them. Therefore, it is of great importance to develop efficient and accurate DTI prediction algorithms. Method: Current algorithms usually represent drugs as extracted features, which are learned by convolutional neural networks (CNNs) from its linear representation, or utilize graph neural networks (GNNs) to learn its graph representation. However, these methods either lose information or fail to capture the structural information of the drug. To address this issue, a novel molecule secondary structure representation network (MSSRN) is proposed to learn drug characterization more accurately. Firstly, the network performs relational graph convolutional networks (R-GCNs) on the drug's molecular graph and integrates drug sequence convolutions to learn the sequential information. Secondly, inspired by the attention mechanism, spatial importance weights of the drug sequence are calculated to guide R-GCNs to learn the topological information of the drug. objective: Identification of drug-target interactions (DTI) Result: A drug-target affinity model, called MSSRN-DTA, was then constructed by using MSSRN to learn drug structure and CNN to learn protein sequence. Conclusion: The effectiveness of the proposed method is verified by comparing it with other alternative methods and baseline models on two benchmark datasets.

中文翻译：

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通过分子二级结构表示网络预测药物靶标亲和力

简介：药物-靶点相互作用（DTI）的鉴定是高特异性、低毒性药物开发的关键步骤。为了加速这一过程，计算机辅助 DTI 预测算法已用于快速筛选化合物或目标。此外，DTI 预测可用于识别现有药物的潜在靶点，从而发现新的适应症并重新定位它们。因此，开发高效、准确的DTI预测算法具有重要意义。方法：当前的算法通常将药物表示为提取的特征，通过卷积神经网络（CNN）从其线性表示中学习，或者利用图神经网络（GNN）来学习其图表示。然而，这些方法要么丢失信息，要么无法捕获药物的结构信息。为了解决这个问题，提出了一种新型分子二级结构表示网络（MSSRN）来更准确地学习药物表征。首先，网络在药物分子图上执行关系图卷积网络（R-GCN），并整合药物序列卷积来学习序列信息。其次，受注意力机制的启发，计算药物序列的空间重要性权重，以指导R-GCN学习药物的拓扑信息。目的：识别药物-靶点相互作用（DTI） 结果：通过使用 MSSRN 学习药物结构和 CNN 学习蛋白质序列，构建药物-靶点亲和力模型，称为 MSSRN-DTA。结论：通过与两个基准数据集上的其他替代方法和基线模型进行比较，验证了所提出方法的有效性。