期刊
MOLECULES
卷 23, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/molecules23092208
关键词
drug-target interaction prediction; machine learning; drug discovery
资金
- National Natural Science Foundation of China [61472333, 61772441, 61472335, 61425002, 81300632]
- Project of marine economic innovation and development in Xiamen [16PFW034SF02]
- Natural Science Foundation of the Higher Education Institutions of Fujian Province [JZ160400]
- Natural Science Foundation of Fujian Province [2017J01099]
- President Fund of Xiamen University [20720170054]
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.
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