ArticleScientific reports2018
AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 1 of them a synthesis that pooled it.
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Who cites it
144 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- Encrypted immunity mediated by peptides.PLoS biology · 2026Review
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An Interpretable Multi-Objective Machine Learning Framework for In Silico Prioritization of Anti-Diagnostics (Basel, Switzerland) · 2026Article
- Dual Effects of a Bothrops asper Phospholipase A₂: Anti-Vibrio Properties and Shrimp Hemocyte Cytotoxicity.Probiotics and antimicrobial proteins · 2026Article
- Article
- Goat gut microbiome as a reservoir for microorganism-encoded short peptides: regulation by host development age and nematode challenge.NPJ biofilms and microbiomes · 2026Article
- Uncovering Key Characteristics of Antibacterial Peptides through Machine Learning.Macromolecular rapid communications · 2026Article
- The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery.Nature communications · 2026Article
- A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.Scientific reports · 2026Article
- CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.Nature communications · 2026Article
- From Membrane Composition to Antimicrobial Strategies: Experimental and Computational Approaches to AMP Design and Selectivity.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- A PLUM Job: Peptide modeLs for Understanding and engineering antiMicrobial therapeutics.bioRxiv : the preprint server for biology · 2026Article
- FungiGuard: identification of plant antifungal peptides with artificial intelligence.Genome biology · 2026Article
- PepFoundry: A Pipeline for Building Machine-Learning Ready Representations of Nonstandard Peptides Containing Cycles, Non-natural Residues, Polymer Units, and More.Journal of chemical information and modeling · 2026Article
- Whole-genome sequencing and comparative genomics reveal antimicrobial potential and adaptive traits of Bacillus velezensis AM12.Functional & integrative genomics · 2026Article
- SGAC: a graph neural network framework for imbalanced and structure-aware AMP classification.Briefings in bioinformatics · 2026Article
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- Article
84 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless, identification of AMPs through wet-lab experiments is still expensive and time consuming. Here, we propose an accurate computational method for AMP prediction by the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Using our collection of large and diverse sets of AMP and non-AMP data (3268 and 166791 sequences, respectively), we evaluated 19 random forest classifiers with different positive:negative data ratios by 10-fold cross-validation. Our optimal model, AmPEP with the 1:3 data ratio, showed high accuracy (96%), Matthew's correlation coefficient (MCC) of 0.9, area under the receiver operating characteristic curve (AUC-ROC) of 0.99, and the Kappa statistic of 0.9. Descriptor analysis of AMP/non-AMP distributions by means of Pearson correlation coefficients revealed that reduced feature sets (from a full-featured set of 105 to a minimal-feature set of 23) can result in comparable performance in all respects except for some reductions in precision. Furthermore, AmPEP outperformed existing methods in terms of accuracy, MCC, and AUC-ROC when tested on benchmark datasets.
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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.