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J Proteome Res. 2017 Apr 7;16(4):1401-1409. doi: 10.1021/acs.jproteome.6b00618. Epub 2017 Mar 13.

Deep-Learning-Based Drug-Target Interaction Prediction.

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College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences , Haikou 571101, PR China.


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.


deep learning; deep-delief network; drug‚ąítarget interaction prediction; feature extraction; semisupervised learning

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