Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a short future outlook of deep learning in DTI prediction is given.

药物-靶标相互作用（DTI）预测在药物发现中起着核心作用。DTI预测中的计算方法已引起更多关注，因为大规模进行体外和体内实验既昂贵又费时。机器学习方法（尤其是深度学习）已广泛应用于DTI预测。在这项研究中，主要目标是提供基于深度学习的DTI预测方法的全面概述。在这里，我们从多个角度研究现有方法。我们探索这些方法以找出哪些深度网络体系结构可用于从药物化合物和蛋白质序列中提取特征。此外，还分析并比较了每种体系结构的优点和局限性。而且，我们探索了如何结合药物和蛋白质特征的描述符的过程。同样，研究了DTI预测中常用的数据集列表。最后，讨论了当前的挑战，并给出了DTI预测中深度学习的短期前景。