DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs
Open accessAbstract
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 DC 50 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 ).
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Recent modeling attempts have been done to predict DC50 and Dmax concentrations of novel PROTACs.
“Recent modeling attempts have been done to predict half maximal (DC50) and maximal degradation (Dmax) concentrations of novel PROTACs.269”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [269]
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- Authors
- Li, F., Hu, Q., Zhang, X., Sun, R., Liu, Z., Wu, S., Tian, S., Ma, X., Dai, Z., Yang, X., Gao, S., & Bai, F.
- Affiliations
- ShanghaiTech University (CN)
- Corresponding author
- Yang, Xiaobao
- Gao, Shenghua
- Bai, Fang
- Venue
- Nature Communications
- Published
- 2022-11-21
- Volume
- 13
- Issue
- 1
- Pages
- 7133-7133
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9681730
- OpenAlex
- W4309650743
- Cited by
- 159
- References
- 91
- Citations by year
- 2026: 47 · 2025: 58 · 2024: 32 · 2023: 22
- FWCI
- 9.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- National Natural Science Foundation of China (#61932020, 20ZR1436000, 82003654, 2018AAA0100704)
- Shanghai Education Development Foundation
- Shanghai Municipal Education Commission
- Science and Technology Commission of Shanghai Municipality (2018AAA0100704, 20ZR1436000, 20QA1406400, 61932020)
- ShanghaiTech University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- PROTACs, targeted protein degradation, E3 ubiquitin ligase, graph convolutional networks, bidirectional LSTM, linker design, structure-activity relationship, web server
- MeSH terms
- Deep Learning, Proteins, Neural Networks, Computer, Ubiquitin-Protein Ligases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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