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Evidence record 1069 · automatically gathered

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

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

Record details

Published: 12 June 2026
Source: arXiv
Category: Research
Topics: Environment · Biotech
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (12 June 2026), “Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs,” evidence record 1069, https://ethics.ai/record/1069 (originally published by arXiv).

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