ReviewMicrobial biotechnology2025
AI Methods for Antimicrobial Peptides: Progress and Challenges.
Review in Microbial biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.Drug delivery and translational research · 2026Review
- Next-Generation Antimicrobial Peptides for Biofilm-Associated Infections: Engineering, Biomaterial Delivery and AI-Assisted Discovery.Antibiotics (Basel, Switzerland) · 2026Review
- Beyond the Barrier: Overcoming Ocular Antimicrobial Resistance Through AI and Novel Therapeutics.International journal of molecular sciences · 2026Review
- From Innate Immunity to Cancer Therapy: Antimicrobial Peptides as Emerging Anticancer Agents.International journal of molecular sciences · 2026Review
- Dual-Peptide Nanoplatform: Mesoporous Silica Nanoparticles Functionalized With a Cell-Penetrating Peptide and Loaded With Rationally Designed Antimicrobial Peptides for Tuberculosis Therapy.Advanced healthcare materials · 2026Article
- Current State of the Fight Against Antimicrobial Resistance: What Are the Different Strategies for Tomorrow?Antibiotics (Basel, Switzerland) · 2026Review
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Generative models for antimicrobial peptide design: auto-encoders and beyond.BioData mining · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.Scientific reports · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Artificial intelligence-driven discovery of bioactive peptides: Computational approaches and future perspectives.aBIOTECH · 2026Review
- Review
- The Future of Antibiotics and Artificial Intelligence: Some Thoughts from Discovery to Bedside.Infectious diseases and therapy · 2026Review
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Article
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Recent Advances in Antimicrobial Proteins and Peptides: A Promising Clinical Breakthrough and Patent Landscape.Current protein & peptide science · 2026Review
- Hybrid and conjugated antimicrobial peptides: new tactics to counter bacterial resistance.Frontiers in microbiology · 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
Abstract
Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure-guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure-guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.
Indexed as
Identifiers
What Socratic 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 Socratic graph.