The Biggest Challenge for Prediction of Membrane Permeability of Cyclic Peptides: Scarce Data Availability

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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Publication Details

Journal
Journal of Medicinal Chemistry
Published
2026-03-03
DOI
https://doi.org/10.1021/acs.jmedchem.6c00507
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
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article

The Biggest Challenge for Prediction of Membrane Permeability of Cyclic Peptides: Scarce Data Availability

Christina Lamers
Journal of Medicinal Chemistry
Machine Learning in Bioinformatics
article

The Biggest Challenge for Prediction of Membrane Permeability of Cyclic Peptides: Scarce Data Availability

Christina Lamers
article en

Abstract

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.

Journal of Medicinal ChemistryVol. 69(5)
Leipzig University (DE)
Openalex Percentile: Top 35%
Machine Learning in Bioinformatics
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