Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability
Wei Liu1, Jianguo Li2,3, Chandra S Verma2,4,5
1Bioinformatics Institute, Agency for Science, Technology and Research, 30 Biopilis Street, Singapore, 138671, Singapore. liuwei@bii.a-star.edu.sg.
Journal of Cheminformatics
|August 28, 2025
Summary
Machine learning models can predict cyclic peptide membrane permeability, aiding drug discovery. Graph-based models like DMPNN show the best performance, improving the identification of cell-permeable drug candidates.
Area of Science:
- Computational chemistry
- Drug discovery
- Machine learning
Background:
- Cyclic peptides are valuable drug candidates for modulating protein-protein interactions.
- Poor membrane permeability hinders their therapeutic use.
- Computational prediction of permeability can streamline drug development.
Purpose of the Study:
- To benchmark machine learning models for predicting cyclic peptide membrane permeability.
- To evaluate different molecular representations and model architectures.
- To assess prediction performance across regression and classification tasks.
Main Methods:
- Evaluated 13 machine learning models using diverse molecular representations (fingerprints, SMILES, graphs, images).
- Utilized PAMPA permeability data for nearly 6000 cyclic peptides from the CycPeptMPDB database.
- Employed random and scaffold splitting strategies to assess model generalizability.
Main Results:
- Model performance varied significantly based on molecular representation and architecture.
- Graph-based models, especially Directed Message Passing Neural Network (DMPNN), demonstrated superior performance.
- Regression tasks generally yielded better results than classification tasks.
- Scaffold splitting indicated lower generalizability than random splitting.
Conclusions:
- Machine learning, particularly graph-based approaches, shows promise for predicting cyclic peptide permeability.
- Current models offer practical value but further improvements are needed.
- Understanding model generalizability is crucial for reliable predictions.
