ArticleSmall science2025
Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.
Article in Small science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Bridging machine learning and evolutionary optimization of threshold specific dosages of Nisin to suppress MRSA biofilm.Antonie van Leeuwenhoek · 2026Article
- Harnessing snake venom cardiotoxins for antimicrobial peptide discovery.npj drug discovery · 2026Article
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- Advancing precision tumor therapy: Progress in targeted delivery of peptide-based nanomaterials from microenvironment to organelles.Materials today. Bio · 2026Review
- Engineering Antimicrobial Peptides via Motif Assembly for Combating Multidrug-Resistant Pathogens.Journal of medicinal chemistry · 2026Article
- AI-driven antimicrobial peptide characterization unveils novel motifs for drug design.Scientific reports · 2025Article
- Antimicrobial peptides for anticancer and antiviral therapy: last promising update.Discover oncology · 2025Review
- A unified model of transient poration induced by antimicrobial peptides.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.