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Graph neural network predicts PROTAC protein degradability

Researchers have developed DegradoMap, a new graph neural network designed to predict the degradability of proteins targeted by PROTACs. Unlike previous methods requiring complete PROTAC structures, DegradoMap uses only protein structure and E3 ligase identity, information available before synthesis. The model demonstrates strong performance on benchmarks, outperforming existing GNN and machine learning baselines, and can also recommend optimal E3 ligases. AI

IMPACT Provides computational guidance for drug discovery, potentially accelerating the identification of effective PROTAC candidates.

RANK_REASON The cluster contains an academic paper describing a new graph neural network model for a specific scientific prediction task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bryan Cheng, Austin Jin ·

    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…