深度学习
计算机科学
人工智能
药物靶点
机器学习
药物发现
过程(计算)
医学
生物信息学
生物
药理学
操作系统
作者
Karim Abbasi,Parvin Razzaghi,Antti Poso,Saber Ghanbari-Ara,Ali Masoudi‐Nejad
标识
DOI:10.2174/0929867327666200907141016
摘要
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.
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