ReviewProbiotics and antimicrobial proteins2026
AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.
Review in Probiotics and antimicrobial proteins, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Harnessing Probiotic LAB and Bacteriocins for Clean-Label Food Processing and Biopreservation: Omics, Molecular Innovations and Industrial Applications.Probiotics and antimicrobial proteins · 2026Review
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Review
- A target-guided drug repurposing strategy for antibacterial discovery.BMC microbiology · 2026Article
- Advances in antimicrobial peptides: promising cancer treatments and vaccines.Frontiers in medicine · 2026Review
- Bacteriocins fromFrontiers in microbiology · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Antimicrobial peptides (AMPs) have emerged as promising alternatives to traditional antibiotics for combating antimicrobial resistance, owing to their unique mechanisms of action and low propensity for resistance development. As antibiotic resistance escalates, there is an urgent need for novel antimicrobial strategies. Artificial intelligence (AI) technologies, particularly machine learning (ML) and deep learning (DL), now offer unprecedented opportunities for accelerating AMP discovery and design. Current AI applications span discriminative models, regression models, generative models, and multimodal optimization, significantly improving screening efficiency, enabling innovative design strategies, and facilitating pre-clinical validation. However, AI-driven AMP research still faces challenges including data quality limitations, model interpretability, and experimental validation bottlenecks. This review systematically summarizes the latest AI advances in AMP research, analyzes key technical hurdles, and outlines future directions and emerging opportunities, providing researchers with comprehensive theoretical and practical guidance to expedite AMP-based drug development.
Indexed as
Identifiers
41273666What 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.