TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
文献类型：期刊论文
| 作者 | Chen, Lifan1,2; Tan, Xiaoqin1,2; Wang, Dingyan1,2; Zhong, Feisheng1,2; Liu, Xiaohong2,3; Yang, Tianbiao1,2; Luo, Xiaomin2 ; Chen, Kaixian2,3; Jiang, Hualiang2,3; Zheng, Mingyue2
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| 刊名 | BIOINFORMATICS
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| 出版日期 | 2020-08-15 |
| 卷号 | 36期号:16页码:4406-4414 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btaa524 |
| 通讯作者 | Jiang, Hualiang(hljiang@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn) |
| 英文摘要 | Motivation: Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance. Results: To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization. |
| WOS关键词 | DRUG-TARGET INTERACTIONS ; CHEMOGENOMICS ; DATABASE ; KERNELS |
| 资助项目 | National Natural Science Foundation of China[81773634] ; National Science & Technology Major Project 'Key New Drug Creation and Manufacturing Program', China[2018ZX09711002] ; 'Personalized Medicines-Molecular Signature-based Drug Discovery and Development', Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12050201] |
| 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:000606794200004 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/296101]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Jiang, Hualiang; Zheng, Mingyue |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai Inst Adv Immunochem Studies, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Lifan,Tan, Xiaoqin,Wang, Dingyan,et al. TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments[J]. BIOINFORMATICS,2020,36(16):4406-4414. |
| APA | Chen, Lifan.,Tan, Xiaoqin.,Wang, Dingyan.,Zhong, Feisheng.,Liu, Xiaohong.,...&Zheng, Mingyue.(2020).TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments.BIOINFORMATICS,36(16),4406-4414. |
| MLA | Chen, Lifan,et al."TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments".BIOINFORMATICS 36.16(2020):4406-4414. |
入库方式： OAI收割
来源：上海药物研究所
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