Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability Article Swipe
Wei Liu
,
Jianguo Li
,
Chandra Verma
,
Hwee Kuan Lee
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13321-025-01083-4
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13321-025-01083-4
Cyclic peptides are promising drug candidates due to their ability to modulate intracellular protein-protein interactions, a property often inaccessible to small molecules. However, their typically poor membrane permeability limits therapeutic applicability. Accurate computational prediction of permeability can accelerate the identification of cell-permeable candidates, reducing reliance on time-consuming and costly experimental screening. Although deep learning has shown potential in predicting molecular properties, its application in permeability prediction remains underexplored. A systematic evaluation of these models is important to assess current capabilities and guide future development. In this study, we conduct a comprehensive benchmark of 13 machine learning models for predicting cyclic peptide membrane permeability. These models cover four types of molecular representations: fingerprints, SMILES strings, molecular graphs, and 2D images. We use experimentally measured PAMPA permeability data from the CycPeptMPDB database, comprising nearly 6000 cyclic peptides, and evaluate performance across three prediction tasks: regression, binary classification, and soft-label classification. Two data-splitting strategies, random split and scaffold split, are used to assess the generalizability of trained models. Our results show that model performance depends strongly on molecular representation and model architecture. Graph-based models, particularly the Directed Message Passing Neural Network (DMPNN), consistently achieve top performance across tasks. Regression generally outperforms classification. Scaffold-based splitting, although intended to more rigorously assess generalization, yields substantially lower model generalizability compared to random splitting. Comparing prediction errors with experimental variability highlights the practical value of current models while also indicating room for further improvement.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13321-025-01083-4
- https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01083-4
- OA Status
- gold
- Cited By
- 1
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413798564
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413798564Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13321-025-01083-4Digital Object Identifier
- Title
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Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeabilityWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-28Full publication date if available
- Authors
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Wei Liu, Jianguo Li, Chandra Verma, Hwee Kuan LeeList of authors in order
- Landing page
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https://doi.org/10.1186/s13321-025-01083-4Publisher landing page
- PDF URL
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https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01083-4Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01083-4Direct OA link when available
- Concepts
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Benchmarking, Computer science, Cyclic peptide, Permeability (electromagnetism), Data mining, Data science, Chemistry, Membrane, Peptide, Biochemistry, Business, MarketingTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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