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Updated: Jan 8, 2026

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Published on: September 19, 2022

PEGASUS: Unlocking Polarity in Cell-Permeable Cyclic Peptides Using AI Models Built on Massively Parallel Biological

Cole Baker¹, Francis A Acquah¹, Lakshmi G Chivukula¹

¹1910, Boston, Massachusetts 02210, United States.

Journal of Medicinal Chemistry

|December 22, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) and a novel assay improve cyclic peptide design for oral therapeutics. PEGASUS, an AI model, enhances cell membrane permeability and aqueous solubility prediction for drug development.

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Area of Science:

Medicinal Chemistry
Computational Biology
Drug Discovery

Background:

Cyclic peptides show therapeutic promise for oral bioavailability but face challenges in cell membrane permeability and solubility.
Current artificial intelligence (AI) approaches for cyclic peptide optimization are limited by scarce, sparse, and hydrophobic-biased biological data.

Purpose of the Study:

To develop a multimodal AI model (PEGASUS) for predicting cyclic peptide cell membrane permeability and guiding the design of orally bioavailable therapeutics.
To overcome data limitations by integrating a high-throughput proxy assay and computational simulations.

Main Methods:

Developed PEGASUS, a multimodal AI model integrating a high-throughput proxy biological assay (1910 PPA) generating billions of cyclic peptides.
Utilized solvent-dependent computational simulations to enhance AI model training data.
Derived design rules for cell-permeable cyclic peptides with high aqueous solubility based on polarity and charge, mimicking FDA-approved drugs.

Main Results:

PEGASUS achieved state-of-the-art performance in predicting cell membrane permeability for cyclic peptides.
The study informed design principles for enhanced cyclic peptide drug candidates.
Successfully designed novel cyclic peptides with improved permeability, incorporating multiple polar or ionizable fragments.

Conclusions:

PEGASUS offers a powerful AI-driven approach to overcome limitations in cyclic peptide drug design.
The developed methodology enables the creation of cell-permeable cyclic peptides with favorable solubility profiles.
This work advances the development of orally bioavailable cyclic peptide therapeutics.