Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks

• URL: http://arxiv.org/abs/2606.04021v1
• Date: Mon, 01 Jun 2026 09:39:22 GMT
• Title: Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks
• Abstract summary: We present DegradoMap, a graph neural network that predicts PROTAC-mediated degradability from protein structure and E3 ligase identity alone.<n>The model encodes biophysical priors through lysine-weighted graph pooling with per-protein normalization.<n>It achieves 0.646+-0.124 AUROC on target-unseen evaluation and 0.811 AUROC on CRBN->VHL E3-unseen transfer.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 through lysine-weighted graph pooling with per-protein normalization, models protein-E3 compatibility via cross-attention, and integrates cellular context from the Cancer Dependency Map. On the PROTAC-8K benchmark (3,101 samples, 155 targets, 10 E3 ligases), DegradoMap achieves 0.646+-0.124 AUROC on target-unseen evaluation (best seed: 0.7449) and 0.811 AUROC on CRBN->VHL E3-unseen transfer, outperforming GNN and machine learning baselines. The model additionally recommends optimal E3 ligases with 74% Hit@3 accuracy. Two findings carry broader implications: E(3)-equivariant architectures underperform the simpler invariant design for this scalar prediction task, and ESM-2 embeddings improve peak performance only with careful regularization -- naive integration fails. DegradoMap provides pre-synthesis computational guidance for degradability assessment; its well-calibrated confidence scores (ECE = 0.029, target-unseen) enable practitioners to prioritize high-confidence predictions for experimental follow-up. However, the high seed variance (std = 0.124) and limited E3 coverage require ensembling for reliable deployment.

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