期刊
CURRENT MEDICINAL CHEMISTRY
卷 28, 期 11, 页码 2100-2113出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/0929867327666200907141016
关键词
Drug-target interaction prediction; Deep learning; Machine learning; Drug discovery; DTIs prediction approaches; EC50
This study provides a comprehensive overview of deep learning-based drug-target interactions (DTIs) prediction approaches, exploring different deep network architectures, commonly used datasets, and current challenges in the field. The research findings reveal the potential of deep learning in DTIs prediction and suggest future directions for development.
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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