TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex
Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein. Despite their therapeutic potential, computational design of molecular glues remains largely unexplored. Unlike conventional structure-based drug design, molecular glue design is governed by the unknown protein-protein interface and requires the simultaneous modeling of ligand generation, protein-protein docking,
Record details
Published: 24 July 2026
Source: arXiv
Category: Research
Topics: Healthcare · Biotech
Retrieved: 27 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex
HuggingFace Daily Papers · 3 August 2026
SoK: Adversarial Robustness of the Variational Quantum Eigensolver via Red-Teaming
arXiv red teaming query · 21 July 2026
Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval
arXiv · 21 July 2026
Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development
arXiv · 21 July 2026
A multi-model prediction of a stage-specific prognosis for colorectal cancer using attention-driven deep ensemble learning on genomic profiling data
Frontiers in Artificial Intelligence · 21 July 2026
PredictRx: AI based decision support tool for molecular screening for breast cancer drug recommendation
Frontiers in Artificial Intelligence · 28 July 2026
How to cite this record
ethics.ai (24 July 2026), “TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex,” evidence record 13621, https://ethics.ai/record/13621 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.