期刊
BRIEFINGS IN BIOINFORMATICS
卷 15, 期 5, 页码 734-747出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbt056
关键词
drug discovery; drug-target interaction prediction; machine learning; drug similarity; target similarity
资金
- National Nature Science Foundation of China [61170097]
- Scientific Research Starting Foundation for Returned Overseas Chinese Scholars, Ministry of Education, China
- Japan Society for the Promotion of Science (JSPS) Invitation Fellowship
- KAKENHI from the Ministry of Education, Culture, Sports, Science and Technology of Japan [23710233, 24300054]
- China Scholarship Council
- Grants-in-Aid for Scientific Research [23710233] Funding Source: KAKEN
Computationally predicting drug-target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug-target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug-target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations.
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