ArticleNature reviews bioengineering2024
Machine learning for antimicrobial peptide identification and design.
Article in Nature reviews bioengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 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
113 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.Drug delivery and translational research · 2026Review
- Peptide-Enabled Nanoplatforms for Malaria and Leishmaniasis: From Intracellular Targeting to Translational Diagnostic Perspectives.ChemMedChem · 2026Review
- Selective α-Amylase Inhibition by Plant Defensins: Structural Determinants, Engineering Strategies, and Translational Prospects.Probiotics and antimicrobial proteins · 2026Review
- Harnessing snake venom cardiotoxins for antimicrobial peptide discovery.npj drug discovery · 2026Article
- Enhancing the Versatility of Polyethylene Terephthalate (PET) Through Strategic Biomolecular Functionalization.Angewandte Chemie (International ed. in English) · 2026Review
- Reframing Antimicrobial Peptides beyond Direct Antimicrobial Activity toward Host-Directed Functions and Disease-Specific Therapeutic Applications.Journal of microbiology and biotechnology · 2026Review
- Marine Antimicrobial Peptides: From Ocean Biodiversity to Genome Mining, Multi-Omics Discovery, and Biotechnological Innovation in the Battle Against Antimicrobial Resistance.Probiotics and antimicrobial proteins · 2026Review
- Vitamin D and antimicrobial peptides in skin health and disease: Mechanisms and therapeutic potential.Biochemistry and biophysics reports · 2026Review
- Anticancer Potential of Antimicrobial Peptides: Mechanisms, Clinical Application, Challenges, and Future Prospects.Drug development research · 2026Review
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- In silico design and In Vitro validation of WK15: a promising antimicrobial peptide for aquaculture applications.World journal of microbiology & biotechnology · 2026Article
- Article
- Natural and Synthetic AMPs from Latin America across Diversity Engineering and Applications.ACS infectious diseases · 2026Review
- Linker Design in Antibody-Drug Conjugates: Balancing Stability and Drug Release.Pharmaceutics · 2026Review
- Time-resolved phenotyping at subcellular resolution reveals shared principles and key trade-offs across antimicrobial peptide activities.bioRxiv : the preprint server for biology · 2026Article
- Complex Networks in Bioactive Peptide Research: A Methodological Review.Biomolecules · 2026Review
- Comparative Biological and Functional Profiling of Single-Position Cysteine Substitutions in the HNP-1-Derived Peptide Pep-H AgainstAntibiotics (Basel, Switzerland) · 2026Article
- Cyclotides from Plants Driving the Next Generation of Antibacterial Agents.Antibiotics (Basel, Switzerland) · 2026Review
- Plant-Derived Peptide-Polymer Therapeutics for Cutaneous Infections and Inflammation: Mechanistic Basis, Delivery Design and Translational Considerations.Pharmaceutics · 2026Review
53 more citing papers are in PubMed but not listed here.
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
Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.
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.