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.