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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 using aredundant internal coordinate representation that generates three-dimensionalconformational ensembles of macrocyclic peptides from their 2D representations.RINGER provides fast backbone and side-chain 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.

Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion