期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 1, 页码 247-269出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz157
关键词
Machine learning; drug-target interaction prediction; DTI software; DTI database
资金
- Michigan Lifestage Environmental Exposures and Disease (M-LEEaD) National Institute of Environmental Health Sciences (NIEHS) Core Center [P30 ES017885]
Predicting interactions between drugs and targets is crucial in drug discovery, necessitating the development of efficient prediction approaches to avoid costly and uncertain experimental determinations. These approaches should timely identify potential drug-target interactions.
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug-target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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