Leveraging large language models for peptide antibiotic design
Large language models (LLMs) have significantly impacted various domains of our society, including recent applications in complex fields such as biology and chemistry. These models, built on sophisticated neural network architectures and trained on extensive datasets, are powerful tools for designing, optimizing, and generating molecules. This review explores the role of LLMs in discovering and designing antibiotics, focusing on peptide molecules. We highlight advancements in drug design and outline the challenges of applying LLMs in these areas.
Authors
- César de la Fuente‐Núñez (ORCID: https://orcid.org/0000-0002-2005-5629)
- Changge Guan (ORCID: https://orcid.org/0000-0003-2531-6821)
- Octávio Luiz Franco (ORCID: https://orcid.org/0000-0001-9546-0525)
- Fabiano C. Fernandes
Institutions
- Universidade Católica de Brasília (BR)
- Instituto Federal de Educação, Ciência e Tecnologia de Brasília (BR)
- Translational Therapeutics (United States) (US)
- Universidade Católica Dom Bosco (BR)
- University of Pennsylvania (US)
Publication Details
- Journal
- Cell Reports Physical Science
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1016/j.xcrp.2024.102359
- Citations
- 13
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- National Institute of General Medical Sciences