Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides
Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad‐spectrum activity and minimum resistance development against the rapidly evolving antibiotic‐resistant pathogens. Herein, to accelerate the discovery process of new AMPs, a predictive and generative algorithm is build, which constructs new peptide sequences, scores their antimicrobial activity using a machine learning (ML) model, identifies amino acid motifs, and assembles high‐ranking motifs into new peptide sequences. The eXtreme Gradient Boosting model achieves an accuracy of ≈87% in distinguishing between AMPs and non‐AMPs. The generated peptide sequences are experimentally validated against the bacterial pathogens, and an accuracy of ≈60% is achieved. To refine the algorithm, the physicochemical features are analyzed, particularly charge and hydrophobicity of experimentally validated peptides. The peptides with specific range of charge and hydrophobicity are then removed, which lead to a substantial increase in an experimental accuracy, from ≈60% to ≈80%. Furthermore, generated peptides are active against different fungal strains with minimal off‐target toxicity. In summary, in silico predictive and generative models for functional motif and AMP discovery are powerful tools for engineering highly effective AMPs to combat multidrug resistant pathogens.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides ; day:06 ; month:03 ; year:2025 ; extent:14
Small science ; (06.03.2025) (gesamt 14)
- Creator
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Bhangu, Sukhvir Kaur
Welch, Nicholas
Lewis, Morgan
Li, Fanyi
Gardner, Brint
Thissen, Helmut
Kowalczyk, Wioleta
- DOI
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10.1002/smsc.202400579
- URN
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urn:nbn:de:101:1-2503061326307.919700608816
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:29 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Bhangu, Sukhvir Kaur
- Welch, Nicholas
- Lewis, Morgan
- Li, Fanyi
- Gardner, Brint
- Thissen, Helmut
- Kowalczyk, Wioleta
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