ReviewInterface focus2017
What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?
Review in Interface focus, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
59 citing papers in PubMed.
- Antimicrobial Peptides Targeting Salmonella enterica serovar Typhi: Mechanism of Action, Structural Insights, and Translational Challenges - A Review.Probiotics and antimicrobial proteins · 2026Review
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence.Environmental microbiology reports · 2025Review
- Marine-Derived N-Terminal Mitochondrial-Targeting Sequences Exhibit Antimicrobial and Anticancer Activities.International journal of molecular sciences · 2025Article
- Dynamics of Th1/Th17 responses and antimicrobial pathways in leprosy skin lesions.The Journal of clinical investigation · 2025Article
- Challenges and Opportunities: Interplay between Infectious Disease and Antimicrobial Resistance in Medical Device Surface Applications.ACS omega · 2025Review
- Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.Small science · 2025Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
- Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.Pathogens (Basel, Switzerland) · 2024Article
- Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.bioRxiv : the preprint server for biology · 2024Article
- Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.Molecular diversity · 2024Review
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Alternative therapeutic strategies to treat antibiotic-resistant pathogens.Nature reviews. Microbiology · 2024Review
- Heterologous Production of Antimicrobial Peptides: Notes to Consider.The protein journal · 2024Article
- Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization.Scientific reports · 2024Article
- Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Feature selection enhances peptide binding predictions for TCR-specific interactions.Frontiers in immunology · 2024Article
- Establishing Quantifiable Guidelines for Antimicrobial α/β-Peptide Design: A Partial Least-Squares Approach to Improve Antimicrobial Activity and Reduce Mammalian Cell Toxicity.ACS infectious diseases · 2023Article
- Design methods for antimicrobial peptides with improved performance.Zoological research · 2023Review
- Molecular hybridization strategy for tuning bioactive peptide function.Communications biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections. We show that machine learning on AMPs can be used to identify essential physico-chemical determinants of AMP functionality, and identify and design peptide sequences to generate membrane curvature. In a broader scope, we discuss the implications of our findings for the discovery of membrane-active peptides in general, and uncovering membrane activity in new and existing peptide taxonomies.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.