文献类型： 外文期刊

作者： Wen, Ming ¹ ; Zhang, Zhimin ¹ ; Niu, Shaoyu ¹ ; Sha, Haozhi ¹ ; Yang, Ruihan ¹ ; Yun, Yonghuan ² ; Lu, Hongmei ¹ ;

作者机构： 1.Cent S Univ, Coll Chem & Chem Engn, Changsha 410083, Hunan, Peoples R China

2.Chinese Acad Trop Agr Sci, Inst Environm & Plant Protect, Haikou 571101, Peoples R China

关键词： deep learning;deep-delief network;feature extraction;drug-target interaction prediction;semisupervised learning

期刊名称：JOURNAL OF PROTEOME RESEARCH （ 影响因子：4.466； 五年影响因子：4.352 ）

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收录情况： SCI

摘要： 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.