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

doi:10.1021/acs.jmedchem.9b00959

	Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
	Xiong, Zhaoping 1,2,3,4; Wang, Dingyan 3,4; Liu, Xiaohong 1,2,3; Zhong, Feisheng 3,4; Wan, Xiaozhe 3,4; Li, Xutong 3,4; Li, Zhaojun 3; Luo, Xiaomin 3; Chen, Kaixian 1,2,3; Jiang, Hualiang 1,2,3
刊名	JOURNAL OF MEDICINAL CHEMISTRY
	2020-08-27
卷号	63 期号:16 页码:8749-8760
ISSN号	0022-2623
DOI	10.1021/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.
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关键词	DATABASE ; QSAR ; HERG
WOS研究方向	Pharmacology & Pharmacy
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000566757500009
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/291060]
专题	中国科学院上海药物研究所
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.