MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules

doi:10.1186/s13321-023-00711-1

KMS > 免疫化学研究所

	MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules
	Liu, Xiaohong1,2,3,4 ; Zhang, Wei 2,3; Tong, Xiaochu 2,3; Zhong, Feisheng 2,3; Li, Zhaojun 4; Xiong, Zhaoping1,2,3 ; Xiong, Jiacheng 2,3; Wu, Xiaolong 2,6; Fu, Zunyun 2; Tan, Xiaoqin 2,3,5; Liu, Zhiguo 4; Zhang, Sulin 2,3; Jiang, Hualiang1,2,3 ; Li, Xutong 2,3; Zheng, Mingyue 2,3,7
	2023-04-08
发表期刊	JOURNAL OF CHEMINFORMATICS (IF:7.1[JCR-2023],9.3[5-Year])
ISSN	1758-2946
卷号	15 期号:1
发表状态	已发表
DOI	10.1186/s13321-023-00711-1
摘要	Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering method, MolFilterGAN, based on a progressively augmented generative adversarial network. Comparative analysis shows that MolFilterGAN outperforms conventional screening approaches based on drug-likeness or synthetic ability metrics. Retrospective analysis of AI-designed discoidin domain receptor 1 (DDR1) inhibitors shows that MolFilterGAN significantly increases the efficiency of molecular triaging. Further evaluation of MolFilterGAN on eight external ligand sets suggests that MolFilterGAN is useful in triaging or enriching bioactive compounds across a wide range of target types. These results highlighted the importance of MolFilterGAN in evaluating molecules integrally and further accelerating molecular discovery especially combined with advanced AI generative models.
关键词	De novo molecule design Generative models Deep learning Virtual screening Compound quality control
URL	查看原文
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China["T2225002","82273855","82204278"] ; Lingang Laboratory["LG202102-01-02","LG-QS-202204-01"] ; National Key Research and Development Program of China[2022YFC3400504] ; China Postdoctoral Science Foundation[2022M720153] ; Youth Innovation Promotion Association CAS[2023296]
WOS研究方向	Chemistry ; Computer Science
WOS类目	Chemistry, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000966066000001
出版者	BMC
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/296032
专题	免疫化学研究所免疫化学研究所_特聘教授组_蒋华良组生命科学与技术学院_博士生
通讯作者	Li, Xutong; Zheng, Mingyue
作者单位	1.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, 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.AlphaMa Inc, 108, Yuxin Rd,Suzhou Ind Pk, Suzhou 215128, Peoples R China 5.ByteDance AI Lab, 1999 Yishan Rd, Shanghai 201103, Peoples R China 6.East China Univ Sci & Technol, Sch Pharm, 130 Meilong Rd, Shanghai 200237, Peoples R China 7.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou 310024, Peoples R China
第一作者单位	免疫化学研究所
第一作者的第一单位	免疫化学研究所
推荐引用方式 GB/T 7714	Liu, Xiaohong,Zhang, Wei,Tong, Xiaochu,et al. MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules[J]. JOURNAL OF CHEMINFORMATICS,2023,15(1).
APA	Liu, Xiaohong.,Zhang, Wei.,Tong, Xiaochu.,Zhong, Feisheng.,Li, Zhaojun.,...&Zheng, Mingyue.(2023).MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules.JOURNAL OF CHEMINFORMATICS,15(1).
MLA	Liu, Xiaohong,et al."MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules".JOURNAL OF CHEMINFORMATICS 15.1(2023).