title: Deep-Learning-Based Drug-Target Interaction Prediction.
abstract: 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.
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
authors.forename: Ming
authors.initials: M
authors.lastname: Wen
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
authors.forename: Zhimin
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
authors.forename: Shaoyu
authors.initials: S
authors.lastname: Niu
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
authors.forename: Haozhi
authors.initials: H
authors.lastname: Sha
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
authors.forename: Ruihan
authors.initials: R
authors.lastname: Yang
authors.affiliation: Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences , Haikou 571101, PR China.
authors.forename: Yonghuan
authors.initials: Y
authors.lastname: Yun
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
authors.forename: Hongmei
authors.identifier: 0000-0002-4686-4491
authors.initials: H
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence / Q000235:genetics; D003198:Computer Simulation; D062313:Databases, Pharmaceutical; D055808:Drug Discovery; D004347:Drug Interactions / Q000235:genetics*; D006801:Humans; D008962:Models, Theoretical; D058990:Molecular Targeted Therapy; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
chemical_list: D011506:Proteins
keywords: deep learning; deep-delief network; drug−target interaction prediction; feature extraction; semisupervised learning