Atom-level Protein Representation Learning Improves Protein Structure Prediction
Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features or alignment targets. Motivated by this, we study protein representations for predicting structures beyond conventional function annotation. We propose TriProRep, a structure-aware pretraining method that jointly models three aligned residue-level views: amino-acid identity, backbone geometry, and local full-atom geometry, discretely encoded via VQ-VAE tokenizers. By pretra
Record details
Published: 21 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Biotech
Retrieved: 14 July 2026
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ethics.ai (21 May 2026), “Atom-level Protein Representation Learning Improves Protein Structure Prediction,” evidence record 3931, https://ethics.ai/record/3931 (originally published by arXiv).
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