期刊
JOURNAL OF PROTEOME RESEARCH
卷 16, 期 4, 页码 1401-1409出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.6b00618
关键词
deep learning; deep-delief network; feature extraction; drug-target interaction prediction; semisupervised learning
资金
- National Natural Science Foundation of China [81402853, 21175157, 21375151, 21305163, 21675174]
- Fundamental Research Funds for the Central University of Central South University [201Szzts163]
Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug target interaction (DTI) can speed up the expensive and time-consuming-experimental work by providing the most potent DTIs. In silico prediction of DTI can-also provide insights about the potential drug drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.
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