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Molecules 2018, 23(9), 2208; https://doi.org/10.3390/molecules23092208

Machine Learning for Drug-Target Interaction Prediction

1
Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China
2
Department of Instrumental and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
*
Author to whom correspondence should be addressed.
Received: 5 August 2018 / Revised: 27 August 2018 / Accepted: 27 August 2018 / Published: 31 August 2018
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
Full-Text   |   PDF [507 KB, uploaded 31 August 2018]   |  

Abstract

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers. View Full-Text
Keywords: drug-target interaction prediction; machine learning; drug discovery drug-target interaction prediction; machine learning; drug discovery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Chen, R.; Liu, X.; Jin, S.; Lin, J.; Liu, J. Machine Learning for Drug-Target Interaction Prediction. Molecules 2018, 23, 2208.

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