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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism

文献类型：期刊论文

作者Xiong, Zhaoping1,2,3,4; Wang, Dingyan3,4; Liu, Xiaohong1,2,3; Zhong, Feisheng3,4; Wan, Xiaozhe3,4; Li, Xutong3,4; Li, Zhaojun3; Luo, Xiaomin3; Chen, Kaixian1,2,3; Jiang, Hualiang1,2,3
刊名JOURNAL OF MEDICINAL CHEMISTRY
出版日期2020-08-27
卷号63期号:16页码:8749-8760
ISSN号0022-2623
DOI10.1021/acs.jmedchem.9b00959
通讯作者Jiang, Hualiang(hljiang@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the rising amounts of data, but the gap between what these neural networks learn and what human beings can comprehend is growing. Moreover, this gap may induce distrust and restrict deep learning applications in practice. Here, we introduce a new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery data sets. We demonstrate that Attentive FP achieves state-of-the-art predictive performances on a variety of data sets and that what it learns is interpretable. The feature visualization for Attentive FP suggests that it automatically learns nonlocal intramolecular interactions from specified tasks, which can help us gain chemical insights directly from data beyond human perception.
WOS关键词DATABASE ; QSAR ; HERG
资助项目National Natural Science Foundation of China[81773634] ; National Natural Science Foundation of China[81430084] ; 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] ; open fund of state key laboratory of Pharmaceutical Biotechnology, Nanjing University, China[KF-GN-201706]
WOS研究方向Pharmacology & Pharmacy
语种英语
WOS记录号WOS:000566757500009
出版者AMER CHEMICAL SOC
源URL[http://119.78.100.183/handle/2S10ELR8/291060]  
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.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 200031, Peoples R China
2.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China
3.Chinese Acad Sci, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai Inst Mat Med, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
4.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Xiong, Zhaoping,Wang, Dingyan,Liu, Xiaohong,et al. Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism[J]. JOURNAL OF MEDICINAL CHEMISTRY,2020,63(16):8749-8760.
APA Xiong, Zhaoping.,Wang, Dingyan.,Liu, Xiaohong.,Zhong, Feisheng.,Wan, Xiaozhe.,...&Zheng, Mingyue.(2020).Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.JOURNAL OF MEDICINAL CHEMISTRY,63(16),8749-8760.
MLA Xiong, Zhaoping,et al."Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism".JOURNAL OF MEDICINAL CHEMISTRY 63.16(2020):8749-8760.

入库方式： OAI收割

来源：上海药物研究所

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