MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model
MultiCycPermea provides an effective tool that accurately predicts the permeability of cyclic peptides, offering valuable insights for improving the membrane permeability of cyclic peptides. This work paves a new path for the application of artificial intelligence in assisting the design of membrane-permeable cyclic peptides.
Authors
- Xiangxiang Zeng (ORCID: https://orcid.org/0000-0001-6201-0114)
- Tetsuya Sakurai (ORCID: https://orcid.org/0000-0002-5789-7547)
- Xiucai Ye (ORCID: https://orcid.org/0000-0002-5547-3919)
- Yifan Shang
- Wencheng Pan (ORCID: https://orcid.org/0009-0004-6742-4710)
- Zixu Wang (ORCID: https://orcid.org/0009-0005-2901-5753)
- Yangyang Chen (ORCID: https://orcid.org/0000-0002-3521-475X)
- Xiulong Yang (ORCID: https://orcid.org/0000-0003-3417-7106)
Institutions
- University of Tsukuba (JP)
- Hunan University (CN)
- Jilin University (CN)
- Central China Normal University (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-02-27
- DOI
- https://doi.org/10.1186/s12915-025-02166-2
- Citations
- 9
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 7.25
Funders
- National Natural Science Foundation of China
- Japan Society for the Promotion of Science