Tang Yuewei, Li Yunhai, Li Pengpai, Liu Zhi-Ping
Center for Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
Curr Med Chem. 2024 Feb 22. doi: 10.2174/0109298673252287240215103035.
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.
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.
A drug-target affinity model, called MSSRN-DTA, was then constructed by using MSSRN to learn drug structure and CNN to learn protein sequence.
The effectiveness of the proposed method is verified by comparing it with other alternative methods and baseline models on two benchmark datasets.
识别药物-靶点相互作用（DTI）是药物开发中具有高特异性和低毒性的关键步骤。为了加速这一过程，计算机辅助的DTI预测算法已被用于快速筛选化合物或靶点。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对其进行重新定位。因此，开发高效准确的DTI预测算法非常重要。
当前的算法通常将药物表示为提取的特征，这些特征由卷积神经网络（CNN）从其线性表示中学习，或者利用图神经网络（GNN）学习其图表示。然而，这些方法要么丢失信息，要么无法捕捉药物的结构信息。为了解决这个问题，提出了一种新颖的分子二级结构表示网络（MSSRN），以更准确地学习药物特征。首先，该网络对药物的分子图执行关系图卷积网络（R-GCN），并整合药物序列卷积以学习序列信息。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。
然后使用MSSRN学习药物结构，CNN学习蛋白质序列，构建了一个名为MSSRN-DTA的药物-靶点亲和力模型。
通过在两个基准数据集上与其他替代方法和基线模型进行比较，验证了所提方法的有效性。
