DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation

doi:10.1093/bfgp/elae011

	DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation
	Gao, Changnan 1; Bao, Wenjie 2; Wang, Shuang 3,4; Zheng, Jianyang 1; Wang, Lulu 1; Ren, Yongqi 1; Jiao, Linfang 1; Wang, Jianmin 5,6,8; Wang, Xun 4,7
	2024-04-06
发表期刊	BRIEFINGS IN FUNCTIONAL GENOMICS
ISSN	2041-2649
页码	12
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.
关键词	molecule generation molecule optimization drug discovery deep learning drug design genetic algorithm
DOI	10.1093/bfgp/elae011
收录类别	SCI
语种	英语
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
WOS类目	Biotechnology & Applied Microbiology ; Genetics & Heredity
WOS记录号	WOS:001197650800001
出版者	OXFORD UNIV PRESS
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38753
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.China Univ Petr East China, Dongying, Peoples R China 2.Peking Univ, Beijing, Peoples R China 3.Ocean Univ China, Coll Comp Sci & Technol, Qingdao, Peoples R China 4.China Univ Petr, Coll Comp Sci & Technol, Qingdao, Peoples R China 5.Yonsei Univ, Seoul, South Korea 6.Chinese Acad Sci, Inst Comp Technol, China High Performance Comp Res Ctr, Beijing, Peoples R China 7.China Univ Petr East China, Inst Comp Sci & Technol, 66 West Changjiang Rd, Qingdao, Peoples R China 8.Yonsei Univ, Dept Integrat Biotechnol, Interdisciplinary Grad Program Integrat Biotechnol, 214 Veritas Hall,85 Songdogwahak Ro, Incheon 21983, South Korea
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.