中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation

文献类型：期刊论文

作者Gao, Changnan8; Bao, Wenjie7; Wang, Shuang5,6; Zheng, Jianyang8; Wang, Lulu8; Ren, Yongqi8; Jiao, Linfang8; Wang, Jianmin1,3,4; Wang, Xun2,5
刊名BRIEFINGS IN FUNCTIONAL GENOMICS
出版日期2024-04-06
页码12
关键词molecule generation molecule optimization drug discovery deep learning drug design genetic algorithm
ISSN号2041-2649
DOI10.1093/bfgp/elae011
英文摘要Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery.
资助项目National Key Research and Development Project of China[2021YFA1000103] ; National Key Research and Development Project of China[2021YFA1000100] ; China National Postdoctoral Program for Innovative Talents[BX2021320] ; National Natural Science Foundation of China[61972416] ; National Natural Science Foundation of China[62272479] ; National Natural Science Foundation of China[62202498] ; Taishan Scholarship[tsqn201812029] ; Foundation of Science and Technology Development of Jinan[201907116] ; Shandong Provincial Natural Science Foundation[ZR2021QF023] ; Fundamental Research Funds for the Central Universities[21CX06018A] ; Spanish project[PID2019-106960GB-I00] ; Juan de la Cierva[IJC2018-038539-I]
WOS研究方向Biotechnology & Applied Microbiology ; Genetics & Heredity
语种英语
WOS记录号WOS:001197650800001
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/38752]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Jianmin; Wang, Xun
作者单位1.Yonsei Univ, Dept Integrat Biotechnol, Interdisciplinary Grad Program Integrat Biotechnol, 214 Veritas Hall,85 Songdogwahak Ro, Incheon 21983, South Korea
2.China Univ Petr East China, Inst Comp Sci & Technol, 66 West Changjiang Rd, Qingdao, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, China High Performance Comp Res Ctr, Beijing, Peoples R China
4.Yonsei Univ, Seoul, South Korea
5.China Univ Petr, Coll Comp Sci & Technol, Qingdao, Peoples R China
6.Ocean Univ China, Coll Comp Sci & Technol, Qingdao, Peoples R China
7.Peking Univ, Beijing, Peoples R China
8.China Univ Petr East China, Dongying, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Changnan,Bao, Wenjie,Wang, Shuang,et al. DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation[J]. BRIEFINGS IN FUNCTIONAL GENOMICS,2024:12.
APA Gao, Changnan.,Bao, Wenjie.,Wang, Shuang.,Zheng, Jianyang.,Wang, Lulu.,...&Wang, Xun.(2024).DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation.BRIEFINGS IN FUNCTIONAL GENOMICS,12.
MLA Gao, Changnan,et al."DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation".BRIEFINGS IN FUNCTIONAL GENOMICS (2024):12.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。