MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model

MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model

Abstract Background Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting “undruggable” proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over the past decade, the FDA has approved an ave...

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Bibliographic Details
Main Authors: Zixu Wang, Yangyang Chen, Yifan Shang, Xiulong Yang, Wenqiong Pan, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng
Format: Article
Language:English
Published: BMC 2025-02-01
Series:BMC Biology
Subjects:
Cyclic peptide
Peptide membrane permeability
Interpretable model
Deep learning
Online Access:https://doi.org/10.1186/s12915-025-02166-2
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https://doi.org/10.1186/s12915-025-02166-2

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