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Macrocyclic peptides are an emerging therapeutic modality, yet computationalapproaches for accurately sampling their diverse 3D ensembles remainchallenging due to their conformational diversity and geometric constraints.Here, we introduce RINGER, a diffusion-based transformer model forsequence-conditioned generation of macrocycle structures based on internalcoordinates. RINGER provides fast backbone sampling while respecting keystructural invariances of cyclic peptides. Through extensive benchmarking andanalysis against gold-standard conformer ensembles of cyclic peptides generatedwith metadynamics, we demonstrate how RINGER generates both high-quality anddiverse geometries at a fraction of the computational cost. Our work lays thefoundation for improved sampling of cyclic geometries and the development ofgeometric learning methods for peptides.

RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion