Machine Learning-Identified Potent Antimicrobial Peptides Against Multidrug-Resistant Bacteria and Skin Infections
ML-driven screening enables rapid identification of two novel candidate AMPs, highlighting the therapeutic potential of GDST peptides for MDR bacterial infections.
Authors
- Nikitha Vavilthota
- Jan W. Drijfhout (ORCID: https://orcid.org/0000-0003-4808-5296)
- Sebastian A. J. Zaat (ORCID: https://orcid.org/0000-0001-9589-186X)
- Martijn Riool (ORCID: https://orcid.org/0000-0002-5444-3179)
- Peter H. Nibbering (ORCID: https://orcid.org/0000-0002-0432-8340)
- Leonie de Boer (ORCID: https://orcid.org/0000-0003-2899-0205)
- Colin Bournez (ORCID: https://orcid.org/0000-0001-5568-4110)
- Gizem Babuççu (ORCID: https://orcid.org/0000-0002-6800-8143)
- Robert A. Cordfunke (ORCID: https://orcid.org/0000-0002-7200-7434)
- Gerard J. P. van Westen (ORCID: https://orcid.org/0000-0003-0717-1817)
Institutions
- Leiden University Medical Center (NL)
- University Hospital Regensburg (DE)
- Centre for Human Drug Research (NL)
- Amsterdam University Medical Centers (NL)
- Institute of Infection and Immunity (CA)
Publication Details
- Journal
- Antibiotics
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3390/antibiotics14111172
- Primary Topic
- Antimicrobial Peptides and Activities
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universiteit Leiden
- Leids Universitair Medisch Centrum