ArticleScientific reports2016
DRAMP: a comprehensive data repository of antimicrobial peptides.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 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
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Who cites it
104 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Musical Peptide: Fragmentation of Musical Protein for AI-driven Bioactive Peptide Discovery Based on a New DeepPepQSAR Pipeline.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- The Activity of Antimicrobial Peptides (AMPs) Identified Via an Integrated in Silico and Pilot in Vitro Screening Approach Towards ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- Role of antimicrobial peptide-based biomaterials in respiratory tract infections control.BMC microbiology · 2026Review
- A Localization-Based Motif Combination Approach To the Design of Antimicrobial Peptides Targeting Gram-Positive and Gram-Negative Bacteria.Probiotics and antimicrobial proteins · 2026Article
- Discovery of antimicrobial peptides targeting Acinetobacter baumannii via a pre-trained and fine-tuned few-shot learning-based pipeline.Nature communications · 2026Article
- D-amino acid substitution and cyclization enhance the stability and antimicrobial activity of arginine-rich peptides.Microbiology (Reading, England) · 2026Article
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- AI-driven antimicrobial peptide characterization unveils novel motifs for drug design.Scientific reports · 2025Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Transfer learning on protein language models improves antimicrobial peptide classification.Scientific reports · 2025Article
- SeekTP: identifying multifunctional therapeutic peptides via graph attention and deep property integration.Briefings in bioinformatics · 2025Article
- Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.Molecular diversity · 2025Review
- Peptides in plant-microbe interactions: Functional diversity and pharmacological applications.Cell surface (Amsterdam, Netherlands) · 2025Review
- Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features.Briefings in bioinformatics · 2025Article
- Article
- Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.Microbial biotechnology · 2025Article
- The intricacies ofFrontiers in microbiology · 2025Review
- Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.Interdisciplinary sciences, computational life sciences · 2024Article
44 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The growing problem of antibiotic-resistant microorganisms results in an urgent need for substitutes to conventional antibiotics with novel modes of action and effective activities. Antimicrobial peptides (AMPs), produced by a wide variety of living organisms acting as a defense mechanism against invading pathogenic microbes, are considered to be such promising alternatives. AMPs display a broad spectrum of antimicrobial activity and a low propensity for developing resistance. Therefore, a thorough understanding of AMPs is essential to exploit them as antimicrobial drugs. Considering this, we developed a comprehensive user-friendly data repository of antimicrobial peptides (DRAMP), which holds 17349 antimicrobial sequences, including 4571 general AMPs, 12704 patented sequences and 74 peptides in drug development. Entries in the database have detailed annotations, especially detailed antimicrobial activity data (shown as target organism with MIC value) and structure information. Annotations also include accession numbers crosslinking to Pubmed, Swiss-prot and Protein Data Bank (PDB). The website of the database comes with easy-to-operate browsing as well as searching with sorting and filtering functionalities. Several useful sequence analysis tools are provided, including similarity search, sequence alignment and conserved domain search (CD-Search). DRAMP should be a useful resource for the development of novel antimicrobial peptide drugs.
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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.