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

Fenglei Li, Qiaoyu Hu, Xianglei Zhang, Renhong Sun, Zhuanghua Liu, Sanan Wu, Siyuan Tian, Xinyue Ma, Zhizhuo Dai, Xiaobao Yang (), Shenghua Gao () and Fang Bai ()
Additional contact information
Fenglei Li: ShanghaiTech University
Qiaoyu Hu: ShanghaiTech University
Xianglei Zhang: ShanghaiTech University
Renhong Sun: Gluetacs Therapeutics (Shanghai) Co., Ltd.
Zhuanghua Liu: ShanghaiTech University
Sanan Wu: ShanghaiTech University
Siyuan Tian: ShanghaiTech University
Xinyue Ma: ShanghaiTech University
Zhizhuo Dai: ShanghaiTech University
Xiaobao Yang: Gluetacs Therapeutics (Shanghai) Co., Ltd.
Shenghua Gao: ShanghaiTech University
Fang Bai: ShanghaiTech University

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract 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 ).

Date: 2022
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DOI: 10.1038/s41467-022-34807-3

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