โก Quick Summary
This study conducted a systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability, utilizing nearly 6000 cyclic peptides from the CycPeptMPDB database. The findings revealed that graph-based models, particularly the Directed Message Passing Neural Network (DMPNN), consistently outperformed other models across various prediction tasks.
๐ Key Details
- ๐ Dataset: Nearly 6000 cyclic peptides from CycPeptMPDB
- ๐งฉ Features used: Four types of molecular representations: fingerprints, SMILES strings, molecular graphs, and 2D images
- โ๏ธ Technology: 13 machine learning models including DMPNN
- ๐ Performance: Regression tasks generally outperformed classification tasks
๐ Key Takeaways
- ๐ Comprehensive evaluation of AI methods for predicting cyclic peptide permeability is crucial for drug development.
- ๐ Graph-based models showed superior performance, with DMPNN leading the pack.
- โ๏ธ Regression tasks yielded better results than binary and soft-label classification tasks.
- ๐ Data-splitting strategies significantly impacted model generalizability, with random splitting performing better than scaffold splitting.
- ๐ Prediction errors were compared with experimental variability, indicating room for improvement in current models.
- ๐ก Insights from this study can guide future developments in permeability prediction for cyclic peptides.
๐ Background
Cyclic peptides are emerging as promising drug candidates due to their unique ability to modulate intracellular protein-protein interactions, a feature often unattainable by small molecules. However, their poor membrane permeability poses a significant challenge to their therapeutic applicability. Accurate computational predictions of permeability can expedite the identification of viable candidates, thereby reducing the need for extensive experimental screening.
๐๏ธ Study
The study systematically evaluated 13 machine learning models for predicting cyclic peptide membrane permeability. Utilizing data from the CycPeptMPDB database, the researchers assessed model performance across three tasks: regression, binary classification, and soft-label classification. The models were tested using two data-splitting strategiesโrandom split and scaffold splitโto evaluate their generalizability.
๐ Results
The results indicated that model performance is heavily influenced by both the molecular representation and the model architecture. The DMPNN model consistently achieved top performance across all tasks, while regression tasks generally outperformed classification tasks. Notably, the scaffold-based splitting method resulted in lower generalizability compared to the random splitting approach.
๐ Impact and Implications
The implications of this study are significant for the field of drug discovery. By enhancing the accuracy of permeability predictions for cyclic peptides, researchers can more effectively identify cell-permeable candidates, ultimately accelerating the development of new therapeutics. This research not only highlights the potential of AI in drug discovery but also sets the stage for future advancements in computational chemistry.
๐ฎ Conclusion
This study underscores the importance of systematic benchmarking in the application of AI for predicting cyclic peptide membrane permeability. The findings advocate for the continued exploration of graph-based models like DMPNN, which have shown remarkable promise. As we move forward, further research is essential to refine these models and enhance their predictive capabilities, paving the way for innovative drug development strategies.
๐ฌ Your comments
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Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability.
Abstract
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.
Author: [‘Liu W’, ‘Li J’, ‘Verma CS’, ‘Lee HK’]
Journal: J Cheminform
Citation: Liu W, et al. Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability. Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability. 2025; 17:129. doi: 10.1186/s13321-025-01083-4