Nature Communications: CycloPepper ML Platform Predicts Cyclization Outcomes and Optimizes Synthesis of Therapeutic Cyclopeptides
A Nature Communications paper (2026) introduces CycloPepper, a machine-learning-guided platform for predicting cyclization outcomes and accelerating automated synthesis of therapeutic cyclic peptides. The model addresses one of the persistent bottlenecks in cyclic-peptide drug development: many promising linear sequences fail at the macrocyclization step or yield poorly under standard conditions, requiring expensive iterative chemistry. CycloPepper trains on a curated dataset of cyclization outcomes and integrates with automated synthesis platforms to enable closed-loop design-make-test cycles. The work joins CyclicMPNN (a fine-tuned ProteinMPNN derivative for cyclic peptide sequence design) and AfCycDesign as part of a fast-maturing computational stack for cyclic-peptide therapeutics.