{
  "id": 1069,
  "url": "https://arxiv.org/abs/2606.19374v1",
  "title": "Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs",
  "summary": "Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding. Proteins instead adopt complex three-dimensional conformations organized around secondary structure elements, such as $α$-helices and $β$-sheets, which encode recurring local motifs and stabilizing hydrogen-bond interactions. In this work, we introduce a secondary-structure",
  "authors": "Mohamed Mouhajir, Limei Wang, El Houcine Bergou, Hajar El Hammouti, Lamiae Azizi, Dongqi Fu",
  "category": "research",
  "topics": "environment,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-12T07:33:44.000Z",
  "fetched_at": "2026-07-14T14:14:59.015Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1069",
  "original_url": "https://arxiv.org/abs/2606.19374v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}