Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer
Membrane permeability is a critical bottleneck in the development of cyclic peptide drugs. Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeabi...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1566174/full |
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| author | Dawei Jiang Zixi Chen Zixi Chen Hongli Du |
| author_facet | Dawei Jiang Zixi Chen Zixi Chen Hongli Du |
| author_sort | Dawei Jiang |
| collection | DOAJ |
| description | Membrane permeability is a critical bottleneck in the development of cyclic peptide drugs. Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. The model demonstrated robust predictive performance, achieving determination coefficients (R2) of 0.67 for PAMPA permeability prediction, and R2 values of 0.75, 0.62, and 0.73 for Caco-2, RRCK, and MDCK cell permeability predictions, respectively. Its performance outperforms traditional machine learning methods and graph-based neural network models. In ablation experiments, we validated the effectiveness of each component in the MAT architecture. Additionally, we analyzed the impact of data pre-training and cyclic peptide conformation optimization on model performance. |
| format | Article |
| id | doaj-art-2a12459e02024704a5db246a8fa2ed9d |
| institution | DOAJ |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj-art-2a12459e02024704a5db246a8fa2ed9d2025-08-20T02:47:46ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-03-01510.3389/fbinf.2025.15661741566174Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformerDawei Jiang0Zixi Chen1Zixi Chen2Hongli Du3School of Biology and Biological Engineering, South China University of Technology, Guangzhou, ChinaDepartment of Gerontology, ShenZhen Longhua District Central Hospital, Shenzhen, ChinaSchool of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, ChinaSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, ChinaMembrane permeability is a critical bottleneck in the development of cyclic peptide drugs. Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. The model demonstrated robust predictive performance, achieving determination coefficients (R2) of 0.67 for PAMPA permeability prediction, and R2 values of 0.75, 0.62, and 0.73 for Caco-2, RRCK, and MDCK cell permeability predictions, respectively. Its performance outperforms traditional machine learning methods and graph-based neural network models. In ablation experiments, we validated the effectiveness of each component in the MAT architecture. Additionally, we analyzed the impact of data pre-training and cyclic peptide conformation optimization on model performance.https://www.frontiersin.org/articles/10.3389/fbinf.2025.1566174/fullcyclic peptidemembrane permeabilitydeep learningmolecular attention transformerpampa |
| spellingShingle | Dawei Jiang Zixi Chen Zixi Chen Hongli Du Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer Frontiers in Bioinformatics cyclic peptide membrane permeability deep learning molecular attention transformer pampa |
| title | Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer |
| title_full | Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer |
| title_fullStr | Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer |
| title_full_unstemmed | Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer |
| title_short | Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer |
| title_sort | cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer |
| topic | cyclic peptide membrane permeability deep learning molecular attention transformer pampa |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1566174/full |
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