Automated design and optimization of multitarget schizophrenia drug candidates by deep learning

doi:10.1016/j.ejmech.2020.112572

	Automated design and optimization of multitarget schizophrenia drug candidates by deep learning
	Tan, Xiaoqin 2,3; Jiang, Xiangrui 1; He, Yang 1; Zhong, Feisheng 2,3; Li, Xutong 2,3; Xiong, Zhaoping 2,4,5; Li, Zhaojun 2,6; Liu, Xiaohong 2,4,5; Cui, Chen 2,3; Zhao, Qingjie 1
刊名	EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
	2020-10-15
卷号	204 页码:16
关键词	Schizophrenia Multitarget antipsychotic drugs Recurrent neural network Multitask deep neural network Automated drug design
ISSN号	0223-5234
DOI	10.1016/j.ejmech.2020.112572
通讯作者	Zheng, Mingyue(myzheng@simm.ac.cn) ; Wang, Zhen(wangzhen@simm.ac.cn) ; Jiang, Hualiang(hljiang@simm.ac.cn)
英文摘要	Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G protein-coupled receptors (GPCRs) to modulate complex neuropsychiatric functions. Here, we report an automated system comprising a deep recurrent neural network (RNN) and a multitask deep neural network (MTDNN) to design and optimize multitarget antipsychotic drugs. The system has successfully generated novel molecule structures with desired multiple target activities, among which high-ranking compound 3 was synthesized, and demonstrated potent activities against dopamine D-2, serotonin 5-HT1A and 5-HT2A receptors. Hit expansion based on the MTDNN was performed, 6 analogs of compound 3 were evaluated experimentally, among which compound 8 not only exhibited specific polypharmacology profiles but also showed antipsychotic effect in animal models with low potential for sedation and catalepsy, highlighting their suitability for further preclinical studies. The approach can be an efficient tool for designing lead compounds with multitarget profiles to achieve the desired efficacy in the treatment of complex neuropsychiatric diseases. (C) 2020 Elsevier Masson SAS. All rights reserved.
资助项目	National Natural Science Foundation of China[81773634] ; National Natural Science Foundation of China[81703338] ; National Science & Technology Major Project Key New Drug Creation and Manufacturing Program, China[2018ZX09711002] ; Personalized Medicinesd Molecular Signature based Drug Discovery and Development, Strategic Priority Research of the Chinese Academy of Sciences[XDA12050201] ; Personalized Medicinesd Molecular Signature based Drug Discovery and Development, Strategic Priority Research of the Chinese Academy of Sciences[XDA12040331]
WOS关键词	ANTIPSYCHOTIC-DRUGS ; DISCOVERY ; PHARMACOLOGY ; DISORDERS ; RECEPTORS ; LIBRARIES ; TARGETS ; UNIQUE
WOS研究方向	Pharmacology & Pharmacy
语种	英语
出版者	ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
WOS记录号	WOS:000573916100012
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/291101]
专题	中国科学院上海药物研究所
通讯作者	Zheng, Mingyue; Wang, Zhen; Jiang, Hualiang
作者单位	1.Chinese Acad Sci, Shanghai Inst Mat Med, CAS Key Lab Receptor Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 2.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 4.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, 393 Huaxiazhong Rd, Shanghai 200031, Peoples R China 5.ShanghaiTech Univ, Sch Life Sci & Technol, 393 Huaxiazhong Rd, Shanghai 200031, Peoples R China 6.Dezhou Univ, Sch Informat Management, 566 West Univ Rd, Dezhou 253023, Peoples R China
推荐引用方式 GB/T 7714	Tan, Xiaoqin,Jiang, Xiangrui,He, Yang,et al. Automated design and optimization of multitarget schizophrenia drug candidates by deep learning[J]. EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY,2020,204:16.
APA	Tan, Xiaoqin.,Jiang, Xiangrui.,He, Yang.,Zhong, Feisheng.,Li, Xutong.,...&Jiang, Hualiang.(2020).Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY,204,16.
MLA	Tan, Xiaoqin,et al."Automated design and optimization of multitarget schizophrenia drug candidates by deep learning".EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 204(2020):16.