AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
Our findings highlight limitations in current deep learning-based representation models. To more accurately capture the properties of antimicrobial peptides (AMPs), it is essential to integrate atomic-level dynamic information that reflects their underlying mechanisms of action.
Authors
- Yanwen Kong
- Ruochi Zhang (ORCID: https://orcid.org/0000-0001-6541-4050)
- Ying Liang
- Fengfeng Zhou (ORCID: https://orcid.org/0000-0002-8108-6007)
- Yuqian Wu (ORCID: https://orcid.org/0000-0001-8664-8980)
- Yinheng Li
- Yan Wang (ORCID: https://orcid.org/0000-0003-2563-255X)
- Yutong Guo (ORCID: https://orcid.org/0009-0003-0988-3428)
- Yusi Fan (ORCID: https://orcid.org/0000-0003-2943-2221)
- Lan Huang (ORCID: https://orcid.org/0000-0003-3233-3777)
- Kewei Li (ORCID: https://orcid.org/0000-0002-2797-576X)
Institutions
- Guiyang Medical University (CN)
- City University of New York (US)
- Jilin University (CN)
- Jilin International Studies University (CN)
- Changchun University (CN)
Publication Details
- Journal
- Journal of Advanced Research
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1016/j.jare.2025.04.046
- Citations
- 4
- Primary Topic
- Antimicrobial Peptides and Activities
- Type
- article
- Field-Weighted Citation Impact
- 4.73