DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs

doi:10.1038/s41467-022-34807-3

	DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs
	Li, Fenglei1,2 ; Hu, Qiaoyu1 ; Zhang, Xianglei1 ; Sun, Renhong 3; Liu, Zhuanghua2 ; Wu, Sanan1 ; Tian, Siyuan1,4 ; Ma, Xinyue1,2 ; Dai, Zhizhuo4 ; Yang, Xiaobao 3; Gao, Shenghua2 ; Bai, Fang1,2,4,5
	2022-11-21
发表期刊	NATURE COMMUNICATIONS
EISSN	2041-1723
卷号	13 期号:1
发表状态	已发表
DOI	10.1038/s41467-022-34807-3
摘要	The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC50 and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server (https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/) and at github (https://github.com/fenglei104/DeepPROTACs).
URL	查看原文
收录类别	SCI
语种	英语
资助项目	Lingang Laboratory[LG202102-01-03] ; Shanghai Science and Technology Development Funds["22ZR1441400","20QA1406400"] ; National Natural Science Foundation of China[82003654] ; National Key R&D Program of China["2018AAA0100704","2020YFA0509700"] ; NSFC[61932020] ; Science and Technology Commission of Shanghai Municipality[20ZR1436000]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000888056100014
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/256375
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_高盛华组生命科学与技术学院_硕士生生命科学与技术学院_博士生免疫化学研究所_PI研究组_白芳组
通讯作者	Yang, Xiaobao; Gao, Shenghua; Bai, Fang
作者单位	1.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China 2.ShanghaiTech Univ, Sch Informat Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China 3.Gluetacs Therapeut Shanghai Co Ltd, 99 Haike Rd,Zhangjiang Hitech Pk, Shanghai 201210, Peoples R China 4.ShanghaiTech Univ, Sch Life Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China 5.Shanghai Clin Res & Trial Ctr, Shanghai 201210, Peoples R China
第一作者单位	免疫化学研究所; 信息科学与技术学院
通讯作者单位	信息科学与技术学院; 免疫化学研究所; 生命科学与技术学院
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	Li, Fenglei,Hu, Qiaoyu,Zhang, Xianglei,et al. DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs[J]. NATURE COMMUNICATIONS,2022,13(1).
APA	Li, Fenglei.,Hu, Qiaoyu.,Zhang, Xianglei.,Sun, Renhong.,Liu, Zhuanghua.,...&Bai, Fang.(2022).DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.NATURE COMMUNICATIONS,13(1).
MLA	Li, Fenglei,et al."DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs".NATURE COMMUNICATIONS 13.1(2022).