Deep-Learning-Based Drug-Target Interaction Prediction

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 i...

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Vydáno v:Journal of proteome research Ročník 16; číslo 4; s. 1401 - 1409
Hlavní autoři: Wen, Ming, Zhang, Zhimin, Niu, Shaoyu, Sha, Haozhi, Yang, Ruihan, Yun, Yonghuan, Lu, Hongmei
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 07.04.2017
Témata:
Algorithms
Amino Acid Sequence - genetics
Computer Simulation
Databases, Pharmaceutical
Drug Discovery
Drug Interactions - genetics
Humans
Models, Theoretical
Molecular Targeted Therapy
Proteins - chemistry
Proteins - genetics
drug−target interaction prediction
deep learning
semisupervised learning
deep-delief network
feature extraction
ISSN:1535-3907
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Shrnutí: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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ISSN:1535-3907
DOI:10.1021/acs.jproteome.6b00618