ArticleNature communications2025
SE(3)-equivariant ternary complex prediction towards target protein degradation.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Evolving CRBN ligands enhance the drug-like properties of protein degraders.Journal of enzyme inhibition and medicinal chemistry · 2026Review
- Advancing PROTAC therapeutics through chemistry-guided design of smart delivery systems.Acta pharmacologica Sinica · 2026Review
- 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
- Autonomously Motile Nano-PROTACs Act as Protein-Sweeping Robots to Enhance Targeted Protein Degradation.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- PROTAC-mediated regulation of programmed cell death: From molecular mechanisms to therapeutic breakthroughs.Innovation (Cambridge (Mass.)) · 2026Review
- Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.Biology direct · 2026Review
- Research progress and future perspectives in proteolysis-targeting chimeras.Frontiers in pharmacology · 2026Review
- In Silico Analysis of Potential Stabilizer Binding Sites at Protein-RNA Interfaces.Computational and structural biotechnology journal · 2026Article
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Advancing PROTAC Discovery Through Artificial Intelligence: Opportunities, Challenges, and Future Directions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- SE(3)-equivariant ternary complex prediction towards target protein degradation.Nature communications · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Targeted protein degradation (TPD) has rapidly emerged as a powerful modality for drugging previously "undruggable" proteins. TPD employs small molecules like PROTACs and molecular glue degraders (MGD) to induce target protein degradation via the formation of a ternary complex with an E3 ligase. However, the rational design of these degraders is severely hindered by the difficulty of obtaining these ternary structures. Here we introduce DeepTernary, a novel end-to-end deep learning approach using an SE(3)-equivariant encoder and a query-based decoder to accurately and rapidly predict these critical structures. Trained on carefully curated TernaryDB, DeepTernary achieves state-of-the-art performance on PROTAC benchmarks without prior exposure to known PROTACs and shows notable prediction capability on the more challenging MGD benchmark with a blind docking protocol. Remarkably, the buried surface areas calculated from predicted structures correlate with experimental degradation potency metrics. Overall, DeepTernary offers a powerful tool for the development of targeted protein degraders.
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