ReviewFrontiers in bioinformatics2026
Recent advances in computational antimicrobial peptide discovery through big data, modeling, and artificial intelligence and their interplay in ushering the next golden era of drug development.
Review in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Recent progress in the structure-activity relationship of antibacterial peptoids.RSC advances · 2026Review
- Microbe-Derived Antimicrobial Peptides: Immunomodulation and Gut Microbiota Homeostasis.Microorganisms · 2026Review
- Rational hybrid design and computational characterization of PA-Hyb1: a novel peptide inhibitor targeting the MexB efflux pump in Pseudomonas aeruginosa.Journal of molecular modeling · 2026Article
- Natural and Synthetic AMPs from Latin America across Diversity Engineering and Applications.ACS infectious diseases · 2026Review
- A Comprehensive Review of Antimicrobial Peptides and Smart Biomaterials in Chronic Wound Therapy: Overcoming Biofilms, Resistance, and Translational Barriers.International journal of molecular sciences · 2026Review
- Antimicrobial and Antibiofilm Activities of Glycyl-Histidine and Methionyl-Glycine Dipeptides: In Vitro and Molecular Docking Studies.Molecules (Basel, Switzerland) · 2026Article
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accelerating antimicrobial resistance (AMR) crisis continues to render more and more conventional antibiotics ineffective. Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics due to their broad-spectrum activity, diverse mechanisms of action, and lower propensity for resistance. Traditional discovery approaches face limitations arising from the vast sequence space and the challenge of balancing efficacy with low toxicity. Addressing these challenges is critical for developing next-generation antimicrobial agents, and computational methods are increasingly driving progress. Public repositories, and techniques such as molecular docking enable
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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 OpenQuestion graph.