ArticleJournal of medicinal chemistry2026
PEGASUS: Unlocking Polarity in Cell-Permeable Cyclic Peptides Using AI Models Built on Massively Parallel Biological Assays.
Article in Journal of medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Linker Engineering in Stapled Peptides for Enhanced Membrane Permeability: Screening and Optimization Strategies.International journal of molecular sciences · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cyclic peptides are a promising class of therapeutics that have the potential for oral bioavailability but are hindered by cell membrane permeability and aqueous solubility. Artificial intelligence (AI) can address the challenging multiparameter optimization of cyclic peptides, but it relies on wet lab ground truth biological data that are scarce, sparse, and dominated by hydrophobic amino acids. Here, we introduce PEGASUS, a multimodal AI model that achieves state-of-the-art performance in predicting cell membrane permeability. PEGASUS integrates an innovative high-throughput proxy biological assay (1910 PPA), which generates billions of cyclic peptides separated by permeability-related characteristics with solvent-dependent computational simulations. PEGASUS informs rules for designing cell-permeable cyclic peptides with high aqueous solubility that resemble FDA-approved therapeutics based on polarity and charge. Combining these rules with a novel generative AI, we design the first published cyclic peptides with more than two polar or ionizable fragments to achieve
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Registered trials
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