Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks
arXiv:2606.04021v1 Announce Type: cross Abstract: Proteolysis-targeting chimeras (PROTACs) can selectively degrade disease-causing proteins, yet predicting which targets are amenable to degradation remains a critical bottleneck: existing computational methods require the complete PROTAC molecular structure, information unavailable before synthesis. We present DegradoMap, a graph neural network that predicts PROTAC-mediated degradability from protein structure and E3 ligase identity alone -- the minimal information available at the target selection stage. The model encodes biophysical priors th
The convergence of advanced AI, particularly Graph Neural Networks (GNNs), with high-throughput biological data generation makes such predictive models suddenly viable, enabling a significant leap in drug discovery efficiency.
This development allows for the prediction of drug degradability early in the discovery process using minimal information, significantly accelerating PROTAC development and broadening the therapeutic landscape for previously undruggable targets.
Drug discovery pipelines for PROTACs can now prioritize targets more effectively before costly synthesis, moving from an empirical trial-and-error approach to a more computationally guided and predictable one.
- · Biopharmaceutical companies pioneering PROTACs
- · AI/ML drug discovery platforms
- · Patients with currently untreatable diseases
- · Traditional high-throughput screening methods
- · Companies slow to adopt AI in drug discovery
The rate of PROTAC drug discovery will increase, leading to more clinical trials in this modality.
An expanded understanding of protein degradation pathways will emerge, driven by AI insights, leading to novel therapeutic strategies.
The success of this AI application could attract significant investment into other AI-driven synthetic biology and drug design initiatives, establishing a new paradigm for therapeutic development.
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Read at arXiv cs.LG