RINGER: Rapid Conformer Generation for Macrocycles with
  Sequence-Conditioned Internal Coordinate Diffusion
Colin A. Grambow Hayley Weir Nathaniel L. Diamant Alex M. Tseng Tommaso Biancalani Gabriele Scalia Kangway V. Chuang
Macrocyclic peptides are an emerging therapeutic modality, yet computational
approaches for accurately sampling their diverse 3D ensembles remain
challenging due to their conformational diversity and geometric constraints.
Here, we introduce RINGER, a diffusion-based transformer model for
sequence-conditioned generation of macrocycle structures based on internal
coordinates. RINGER provides fast backbone sampling while respecting key
structural invariances of cyclic peptides. Through extensive benchmarking and
analysis against gold-standard conformer ensembles of cyclic peptides generated
with metadynamics, we demonstrate how RINGER generates both high-quality and
diverse geometries at a fraction of the computational cost. Our work lays the
foundation for improved sampling of cyclic geometries and the development of
geometric learning methods for peptides.