MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network
文献类型：期刊论文
| 作者 | Zhou, Deshan1; Xu, Zhijian2 ; Li, WenTao3; Xie, Xiaolan4; Peng, Shaoliang1,3
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| 刊名 | BIOINFORMATICS
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| 出版日期 | 2021-12-01 |
| 卷号 | 37期号:23页码:4485-4492 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btab473 |
| 通讯作者 | Xu, Zhijian(zjxu@simm.ac.cn) ; Xie, Xiaolan(xie_xiao_lan@foxmail.com) ; Peng, Shaoliang(slpeng@hnu.edu.cn) |
| 英文摘要 | Motivation: Predicting new drug-target interactions is an important step in new drug development, understanding of its side effects and drug repositioning. Heterogeneous data sources can provide comprehensive information and different perspectives for drug-target interaction prediction. Thus, there have been many calculation methods relying on heterogeneous networks. Most of them use graph-related algorithms to characterize nodes in heterogeneous networks for predicting new drug-target interactions (DTI). However, these methods can only make predictions in known heterogeneous network datasets, and cannot support the prediction of new chemical entities outside the heterogeneous network, which hinder further drug discovery and development. Results: To solve this problem, we proposed a multi-modal DTI prediction model named 'MultiDTI' which uses our proposed joint learning framework based on heterogeneous networks. It combines the interaction or association information of the heterogeneous network and the drug/target sequence information, and maps the drugs, targets, side effects and disease nodes in the heterogeneous network into a common space. In this way, 'MultiDTI' can map the new chemical entity to this learned common space based on the chemical structure of the new entity. That is, bridging the gap between new chemical entities and known heterogeneous network. Our model has strong predictive performance, and the area under the receiver operating characteristic curve of the model is 0.961 and the area under the precision recall curve is 0.947 with 10-fold cross validation. In addition, some predicted new DTIs have been confirmed by ChEMBL database. Our results indicate that 'MultiDTI' is a powerful and practical tool for predicting new DTI, which can promote the development of drug discovery or drug repositioning. |
| 资助项目 | National Key R&D Program of China[2017YFB0202602] ; National Key R&D Program of China[2018YFC0910405] ; National Key R&D Program of China[2017YFC1311003] ; National Key R&D Program of China[2016YFC1302500] ; National Key R&D Program of China[2016YFB0200400] ; National Key R&D Program of China[2017YFB0202104] ; NSFC[U19A2067] ; NSFC[61772543] ; NSFC[U1435222] ; NSFC[61625202] ; NSFC[61272056] ; Funds of Peng Cheng Lab, State Key Laboratory of Chemo/Biosensing and Chemometrics ; Fundamental Research Funds for the Central Universities ; Guangdong Provincial Department of Science and Technology[2016B090918122] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000733374500025 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/300830]  |
| 专题 | 中国科学院上海药物研究所 |
| 通讯作者 | Xu, Zhijian; Xie, Xiaolan; Peng, Shaoliang |
| 作者单位 | 1.Hunan Univ, Dept Comp Sci, Changsha 410082, Peoples R China 2.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, CAS Key Lab Receptor Res, Shanghai 201203, Peoples R China 3.Natl Univ Def Technol, Dept Comp Sci, Changsha 410073, Peoples R China 4.Guilin Univ Technol, Coll Informat Sci & Engn, Guilin 541004, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Deshan,Xu, Zhijian,Li, WenTao,et al. MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network[J]. BIOINFORMATICS,2021,37(23):4485-4492. |
| APA | Zhou, Deshan,Xu, Zhijian,Li, WenTao,Xie, Xiaolan,&Peng, Shaoliang.(2021).MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network.BIOINFORMATICS,37(23),4485-4492. |
| MLA | Zhou, Deshan,et al."MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network".BIOINFORMATICS 37.23(2021):4485-4492. |
入库方式： OAI收割
来源：上海药物研究所
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