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Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs

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arXiv:2606. 19374v1 Announce Type: new Abstract: 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.

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Authors: Mohamed Mouhajir, Limei Wang, El Houcine Bergou, Hajar El Hammouti, Lamiae Azizi, Dongqi Fu
Released: 2026-06-19 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, alpha, eval, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.19374

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