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Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives

Author(s): Karim Abbasi, Parvin Razzaghi, Antti Poso, Saber Ghanbari-Ara and Ali Masoudi-Nejad*

Volume 28 , Issue 11 , 2021

Published on: 07 September, 2020

Page: [2100 - 2113] Pages: 14

DOI: 10.2174/0929867327666200907141016

Price: $65

Abstract

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.

Keywords: Drug-target interaction prediction, Deep learning, Machine learning, Drug discovery, DTIs prediction approaches, EC50.


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