ArticleMethods in molecular biology (Clifton, N.J.)2024
Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 23 citations in OpenAlex.
- Avian β-defensin 1 as an antibiotic-sparing supplement for preserving rooster semen quality and fertility during chilled storage.Poultry science · 2026Article
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- The Role of Flexibility in the Bioactivity of Short α-Helical Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Exploring the potential of anticancer peptides as therapeutic agents for cancer treatment.Research in pharmaceutical sciences · 2025Review
- Enhancing Antimicrobial Peptides from Frog Skin: A Rational Approach.Biomolecules · 2025Article
- Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
- [Tuberculosis: Analysis of the history and development of multiple antibiotic resistance].Revista medica del Instituto Mexicano del Seguro Social · 2024Review
- Recent Progress in Terrestrial Biota Derived Antibacterial Agents for Medical Applications.Molecules (Basel, Switzerland) · 2024Review
- A Critical Review of Short Antimicrobial Peptides from Scorpion Venoms, Their Physicochemical Attributes, and Potential for the Development of New Drugs.The Journal of membrane biology · 2024Review
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Novel technologies uncover novel 'anti'-microbial peptides inPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota.Microbial ecology · 2023Review
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 at 4 institutions in 3 countries.
Funding
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
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.