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Updated: Jun 13, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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

Huitian Lin¹, Cheng Zhu¹, Tianfeng Shang²

¹College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China.

Journal of Medicinal Chemistry

|May 23, 2025

Summary

HighPlay, a novel AI approach, designs cyclic peptides for therapeutics by optimizing sequences and binding sites using reinforcement learning and structural prediction. This method accelerates drug discovery, showing promising binding affinity in simulations and experiments.

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Constructing Cyclic Peptides Using an On-Tether Sulfonium Center

Constructing Cyclic Peptides Using an On-Tether Sulfonium Center

Published on: September 28, 2022

Area of Science:

Computational chemistry and drug discovery
Artificial intelligence in molecular design
Peptide therapeutics

Background:

Cyclic peptides offer structural diversity and biocompatibility, making them promising therapeutic agents.
Current design methods are often experimental, costly, and time-consuming, limiting molecular diversity.
AI-assisted methods still face challenges in efficiency and scope.

Purpose of the Study:

To introduce HighPlay, an integrated approach for designing cyclic peptide sequences.
To achieve synergistic optimization of cyclic peptide sequences and their binding sites.
To dynamically explore sequence space without predefined target information.

Main Methods:

Integration of reinforcement learning (Monte Carlo Tree Search) with the HighFold structural prediction model.
Design of cyclic peptide sequences based solely on target protein sequence information.
Screening and verification using molecular dynamics simulations.

Main Results:

Successful application of HighPlay to design cyclic peptides for three distinct protein targets.
Demonstrated good binding affinity for designed cyclic peptides via simulations.
Experimental validation confirmed micromolar-level affinity for TEAD4-targeting cyclic peptides.

Conclusions:

HighPlay offers an efficient, AI-driven method for de novo cyclic peptide design.
The approach enables synergistic optimization of peptide sequences and binding sites.
Validated cyclic peptides show therapeutic potential, particularly for targets like TEAD4.