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

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 in vitro cell
membrane permeability.