School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China.
School of Computer Science and Engineering, Dalian Minzu University, Dalian,116600, China.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac434.
Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently.
In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs.
发现药物-靶标相互作用（DTIs）是药物开发的关键步骤，例如识别药物副作用和药物重新定位。由于通过网络生物学实验识别 DTIs 既耗时又昂贵，因此已经提出了许多基于计算的方法，并已成为推断潜在相互作用的有效方法。尽管为解决这一任务投入了大量精力，但预测准确性仍有待提高。更特别是，基于异构网络的方法并没有充分考虑这些异构网络中的复杂结构和丰富的语义信息。因此，高效地预测 DTIs 仍然是一个挑战。
在这项研究中，我们通过多层次注意力机制的多视图异构信息网络嵌入开发了一种新方法，以发现潜在的药物-靶标相互作用（MHADTI）。首先，MHADTI 通过利用药物和靶标多源信息，为药物和靶标构建不同的相似性网络。结合已知的 DTI 网络，建立了三个具有不同视图的药物-靶标异构信息网络（HIN）。其次，MHADTI 利用层次注意力机制从多视图 HIN 中学习药物和靶标的嵌入，其中包括节点级、语义级和图级注意力。最后，MHADTI 采用多层感知机利用学习到的深度特征表示来预测 DTIs。层次注意力机制可以充分考虑节点、元路径和图在学习药物和靶标特征表示中的重要性，从而使它们的嵌入更加全面。大量实验结果表明，MHADTI 优于其他 SOTA 预测模型。此外，对一些感兴趣的药物和靶标的预测结果分析进一步表明，MHADTI 在发现 DTIs 方面具有优势。
