ArticleNature communications2022
DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
What it found
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Who cites it
67 citing papers in PubMed.
- Context matters in PROTAC design: navigating the trade-off between degradation and developability.Journal of enzyme inhibition and medicinal chemistry · 2026Review
- DesignMaster: a multi-conditional diffusion framework for rational PROTAC design.Bioinformatics (Oxford, England) · 2026Article
- From Ternary Modeling to Predictive PROTAC Design: A Computational Perspective.Journal of chemical information and modeling · 2026Article
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- Pocket-PROTACs: an interpretable pocket-aware deep learning framework for predicting PROTAC-induced protein degradation.Bioinformatics (Oxford, England) · 2026Article
- Review
- Design of Synthesizable PROTACs through Synthesis Constrained Generative Model and Reinforcement Learning.JACS Au · 2026Article
- A quantitative approach for defining the degradability landscape of protein degraders.Nature communications · 2026Article
- Article
- ESCRT-Mimetic Nanodegrader Targets STING for Anti-Inflammatory Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.Biology direct · 2026Review
- Methods to Study the Molecular Mechanism and Drive the Design of Protein Degraders.Chemical reviews · 2026Review
- Bellerophon: An Automated Tool for PROTAC Decomposition.ACS medicinal chemistry letters · 2026Article
- Breaking the bonds: targeting protein dimerization for prostate cancer therapy.Endocrinology · 2026Review
- SE(3)-PROTACs: Geometric deep learning for PROTAC degradation prediction.Briefings in bioinformatics · 2026Article
- Integrating AI into next-generation PROTAC Engineering: a comprehensive toolkit for rational PROTAC design.Molecular diversity · 2026Review
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- DeepDegradome: A structure-aware deep learning framework for PROTAC and ligand generation against protein targets.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- PROTAC-Splitter: a machine learning framework for automated identification of PROTAC substructures.Journal of cheminformatics · 2026Article
- A machine learning-based pharmacokinetics predictor (EGFR-PROPK) for EGFR-targeting PROTACs.Communications chemistry · 2026Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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 DC
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.