HighPlay: Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction

Abstract The structural diversity and good biocompatibility of cyclic peptides has led to their emergence as potential therapeutic agents. Traditional cyclic peptide design relies on natural template modification and combinatorial chemical library screening, but suffers from bottlenecks such as limited molecular diversity, high cost, and time-consuming.AI technologies have improved design efficiency by predicting target binding patterns and generating scaffold structures, but they still require extensive laboratory screening. In this study, we propose HighPlay, which integrates reinforcement learning (Monte Carlo Tree Search) with the HighFold structure prediction model to design cyclic peptide sequences for protein targets, dynamically exploring the sequence space without the need of predefined target information. The model was applied to the design of cyclic peptide sequences for three different targets, which were screened and verified by molecular dynamics simulation, and showed good binding affinity. Specifically, the cyclic peptide sequences designed for TEAD4 target showed micromolar-level affinity in further experimental validation.