Detalle de: Deep-learning dive into the antimicrobial potential of Archaea
| Title | Deep-learning dive into the antimicrobial potential of Archaea |
| Abstract |
The global burden of antimicrobial resistance demands the urgent development of new antibiotics. To face this threat, Torres et al. leveraged deep-learning models to identify archaeal encrypted peptides (termed 'archaeasins') with potential antimicrobial activity. This work highlights Archaea as an underexplored but potentially rich source of antibiotics. |
| Apa Reference | Rodríguez-Gijón, A. & Laso-Pérez, R. (2026). Deep-learning dive into the antimicrobial potential of Archaea. Trends in Microbiology, 34(2), 123-125. 10.1016/j.tim.2025.12.004 |
| Doi | 10.1016/j.tim.2025.12.004 |
| Publication Date | Feb 2026 |
| Publication Type | Article |
| Researcher | Alejandro Rodríguez Gijón |
| Ontology | Drug resistance |
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