Generative Models for De Novo Drug Design

doi:10.1021/acs.jmedchem.1c00927

	Generative Models for De Novo Drug Design
	Tong, Xiaochu 1,2; Liu, Xiaohong 1,2; Tan, Xiaoqin 1,2; Li, Xutong 1,2; Jiang, Jiaxin 1,2; Xiong, Zhaoping 3; Xu, Tingyang 4; Jiang, Hualiang 1,2; Qiao, Nan 3; Zheng, Mingyue 1,2
刊名	JOURNAL OF MEDICINAL CHEMISTRY
	2021-10-14
卷号	64 期号:19 页码:14011-14027
ISSN号	0022-2623
DOI	10.1021/acs.jmedchem.1c00927
通讯作者	Jiang, Hualiang(hljiang@simm.ac.cn) ; Qiao, Nan(qiaonan3@huawei.com) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要	Artificial intelligence (AI) is booming. Among various AI approaches, generative models have received much attention in recent years. Inspired by these successes, researchers are now applying generative model techniques to de novo drug design, which has been considered as the "holy grail" of drug discovery. In this Perspective, we first focus on describing models such as recurrent neural network, autoencoder, generative adversarial network, transformer, and hybrid models with reinforcement learning. Next, we summarize the applications of generative models to drug design, including generating various compounds to expand the compound library and designing compounds with specific properties, and we also list a few publicly available molecular design tools based on generative models which can be used directly to generate molecules. In addition, we also introduce current benchmarks and metrics frequently used for generative models. Finally, we discuss the challenges and prospects of using generative models to aid drug design.
资助项目	National Natural Science Foundation of China[81773634] ; National Science & Technology Major Project Key New Drug Creation and Manufacturing Program of China[2018ZX09711002-001-003] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12020372] ; Tencent AI Lab Rhino-Bird Focused Research Program[JR202002] ; Shanghai Municipal Science and Technology Major Project
WOS关键词	CHEMICAL UNIVERSE ; GENETIC ALGORITHM ; MOLECULE GENERATION ; DATABASE ; INFORMATION ; IDENTIFICATION ; EXPLORATION ; DISCOVERY ; LIBRARIES ; SYSTEMS
WOS研究方向	Pharmacology & Pharmacy
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000709633100005
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/298698]
专题	中国科学院上海药物研究所
通讯作者	Jiang, Hualiang; Qiao, Nan; Zheng, Mingyue
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China 3.Huawei Technol Co Ltd, Lab Hlth Intelligence, Shenzhen 518100, Peoples R China 4.Tencent AI Lab, Shenzhen 518057, Peoples R China
推荐引用方式 GB/T 7714	Tong, Xiaochu,Liu, Xiaohong,Tan, Xiaoqin,et al. Generative Models for De Novo Drug Design[J]. JOURNAL OF MEDICINAL CHEMISTRY,2021,64(19):14011-14027.
APA	Tong, Xiaochu.,Liu, Xiaohong.,Tan, Xiaoqin.,Li, Xutong.,Jiang, Jiaxin.,...&Zheng, Mingyue.(2021).Generative Models for De Novo Drug Design.JOURNAL OF MEDICINAL CHEMISTRY,64(19),14011-14027.
MLA	Tong, Xiaochu,et al."Generative Models for De Novo Drug Design".JOURNAL OF MEDICINAL CHEMISTRY 64.19(2021):14011-14027.