PepEVOLVE: Position-Aware Dynamic Peptide Optimization via Group-Relative Advantage

PepEVOLVE is a dynamic, position‑aware framework for optimizing macrocyclic peptides that learns where to edit and how to improve multiple properties. It enhances pretraining with dynamic masking and CHUCKLES shifting, employs a multi‑armed‑bandit router to identify high‑reward residues, and uses an evolving optimization algorithm with group‑relative advantage to stabilize updates. In in‑silico tests on a Rev‑binding macrocycle benchmark, PepEVOLVE surpassed PepINVENT, achieving higher permeability‑weighted scores and converging faster while respecting structural constraints.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• reinforcement-learning
• benchmarks