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

文献类型：期刊论文

作者Tan, Xiaoqin1,2; Jiang, Xiangrui3; He, Yang3; Zhong, Feisheng1,2; Li, Xutong1,2; Xiong, Zhaoping1,4,5; Li, Zhaojun1,6; Liu, Xiaohong1,4,5; Cui, Chen1,2; Zhao, Qingjie3
刊名EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
出版日期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
DOI10.1016/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.
WOS关键词ANTIPSYCHOTIC-DRUGS ; DISCOVERY ; PHARMACOLOGY ; DISORDERS ; RECEPTORS ; LIBRARIES ; TARGETS ; UNIQUE
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研究方向Pharmacology & Pharmacy
语种英语
WOS记录号WOS:000573916100012
出版者ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
源URL[http://119.78.100.183/handle/2S10ELR8/291101]  
专题新药研究国家重点实验室
通讯作者Zheng, Mingyue; Wang, Zhen; Jiang, Hualiang
作者单位1.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Shanghai Inst Mat Med, CAS Key Lab Receptor Res, 555 Zuchongzhi Rd, Shanghai 201203, 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.

入库方式： OAI收割

来源：上海药物研究所

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