DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs
Fenglei Li
(Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University)
Qiaoyu Hu
(Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University)
Xianglei Zhang
(Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University)
Renhong Sun
(Gluetacs Therapeutics (Shanghai) Co., Ltd.)
Zhuanghua Liu
(School of Information Science and Technology, ShanghaiTech University)
Sanan Wu
(Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University)
Siyuan Tian
(Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University)
Xinyue Ma
(Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University)
Zhizhuo Dai
(School of Life Science and Technology, ShanghaiTech University)
Xiaobao Yang
(Gluetacs Therapeutics (Shanghai) Co., Ltd.)
Shenghua Gao
(School of Information Science and Technology, ShanghaiTech University)
Fang Bai
(Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University)
The rational design of PROTACs is difficult due to their obscure structure-activity relationship. Here the authors present a deep neural network model - DeepPROTACs - for predicting the degradation capacity of a proposed PROTAC molecule.
Fenglei Li, Qiaoyu Hu, Xianglei Zhang, et al. (2022). "DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs". Nature Communications. https://doi.org/10.1038/s41467-022-34807-3