AI Methods for Antimicrobial Peptides: Progress and Challenges
Author Information
Author(s): Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes, Cesar de la Fuente‐Nunez
Primary Institution: University of Pennsylvania
Hypothesis
AI methods can significantly enhance the discovery and design of antimicrobial peptides (AMPs).
Conclusion
AI techniques, particularly machine learning and deep learning, are revolutionizing the identification and design of antimicrobial peptides, addressing challenges in combating multidrug-resistant pathogens.
Supporting Evidence
- AI methods have accelerated the discovery of new peptides with anti-infective activity.
- Classical machine learning approaches are being replaced by deep learning models.
- Existing reviews have not thoroughly explored the potential of large language models and graph neural networks.
Takeaway
Scientists are using computers to help find new medicines that can fight germs. This is important because some germs are getting stronger and harder to beat.
Methodology
The review discusses various AI techniques, including machine learning and deep learning, used for the discovery and design of antimicrobial peptides.
Potential Biases
There is a risk of bias due to the selection of training datasets and the lack of experimental standardization in AMP studies.
Limitations
The review highlights that many existing studies have not fully explored the potential of large language models and graph neural networks in AMP discovery.
Digital Object Identifier (DOI)
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