Modification-aware AI enables terminal chemical modifications for peptide design and discovers potent antimicrobials
experiments, confirming their therapeutic potential. Collectively, these results highlight the high hit rate, biological relevance, and translational promise of this integrative, modification-aware AI framework for next-generation antimicrobial peptide development.
Authors
- Jiangning Song (ORCID: https://orcid.org/0000-0001-8031-9086)
- Fuyi Li (ORCID: https://orcid.org/0000-0001-5216-3213)
- Marcelo D. T. Torres (ORCID: https://orcid.org/0000-0002-6165-9138)
- Jin Xu (ORCID: https://orcid.org/0000-0002-9997-8150)
- Chen Li (ORCID: https://orcid.org/0000-0002-1847-754X)
- Jian Li
Institutions
- Discovery Institute (US)
- Australian Regenerative Medicine Institute (AU)
- Translational Therapeutics (United States) (US)
- Monash University (AU)
- The University of Adelaide (AU)
- Monash Institute of Medical Research (AU)
- University of Pennsylvania (US)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-04-10
- DOI
- https://doi.org/10.64898/2026.04.09.717597
- Primary Topic
- Antimicrobial Peptides and Activities
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Defense Threat Reduction Agency
- Australian Research Council
- National Health and Medical Research Council
- National Institute of General Medical Sciences