This viewpoint discusses how limited biological data affects predicting permeability in cyclic peptides, implying a need for improved data collection.
AI-based multiparameter optimization for membrane permeability of cyclic peptides is hampered by the limited availability of ground truth biological data. This Viewpoint highlights the Featured Article reporting an AI model, integrating a high-throughput assay classifying billions of cyclic peptides for permeability-related characteristics. Combining this with a generative AI, the first de novo designed permeable cyclic peptides containing polar groups were reported.
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Christina Lamers (2026) studied this question.
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