Title: Model Agnostic Conditioning of Boltzmann Generators for Peptide Cyclization
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Peptide Design, AI, BioML, Cyclic peptides, Protein Dynamics, Flow matching, Boltzmann generators

Abstract: Macrocyclic peptides offer strong therapeutic potential due to their enhanced binding affinity and protease resistance, but their design remains a challenge due to limited structural data and tools that address only a narrow set of cyclization chemistries. Moreover, existing models are built to only consider ground state or mean conformations, rather than conformational ensembles that more accurately describes peptides. We introduce CycLOPS (a Cyclic Loss for the Optimization of Peptide Structures), a model-agnostic framework that conditions Boltzmann generators to sample valid cyclic conformations—without retraining. To overcome the scarcity of cyclic peptide data, we reformulate the design problem in terms of conditional sampling over linear peptide structures via chemically informed loss functions. CycLOPS encompasses 18 possible inter-amino acid crosslinks enabled by 6 diverse chemical reactions, and is readily extensible to many more. It leverages tetrahedral geometry constraints, using six interatomic distances to define a kernel density-estimated joint distribution from MD simulations. We demonstrate CycLOPS’s versatility via two distinct generative models: a modified Sequential Boltzmann Generator (SBG) (Tan et al., 2025) and the Equivariant Normalizing flow (ECNF) of Klein & Noé (2024). In both settings, CycLOPS successfully biases the Boltzmann distribution toward chemically plausible macrocycles.

TL;DR: We condition CNF and DNF-based Boltzmann generators for cyclic peptide design.