AI RESEARCH
PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
arXiv CS.LG
•
ArXi:2605.01625v1 Announce Type: new Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology. However, existing protein representation learning methods typically operate at a single structural level or treat different sources of structural information as parallel modalities, without explicitly modeling their hierarchical relationships. We