ArticleBMC research notes2023
Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction.
Article in BMC research notes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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Who cites it
17 citing papers in PubMed.
- Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.Drug delivery and translational research · 2026Review
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- Review
- AMPSeek: A Workflow for Predicting Antimicrobial Peptide Activity, Three-Dimensional Structure, and Toxicity.Current protocols · 2026Article
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- NMR Unveils Activity Mechanism of Linear Spider Venom Peptide Fragments Selected by Neural Networks Against Staphylococci Including MRSA.Pharmaceutics · 2025Article
- Screening and Validation of Functional Residues of the Antimicrobial PeptideBiomolecules · 2025Article
- Deep-Learning Driven Identification of Novel Antimicrobial Peptides.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Mining the UniProtKB/Swiss-Prot database for antimicrobial peptides.Protein science : a publication of the Protein Society · 2025Article
- Peptipedia v2.0: a peptide sequence database and user-friendly web platform. A major update.Database : the journal of biological databases and curation · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
- De novo synthetic antimicrobial peptide design with a recurrent neural network.Protein science : a publication of the Protein Society · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides.PloS one · 2024Article
- The NIH Comparative Genomics Resource: addressing the promises and challenges of comparative genomics on human health.BMC genomics · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectivesAntibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs). Recently, we have reported AMPlify, an attentive deep learning model for predicting AMPs in databases of peptide sequences. In our tests, AMPlify outperformed the state-of-the-art. We have illustrated its use on data describing the American bullfrog (Rana [Lithobates] catesbeiana) genome. Here we present the model files and training/test data sets we used in that study. The original model (the balanced model) was trained on a balanced set of AMP and non-AMP sequences curated from public databases. In this data note, we additionally provide a model trained on an imbalanced set, in which non-AMP sequences far outnumber AMP sequences. We note that the balanced and imbalanced models would serve different use cases, and both would serve the research community, facilitating the discovery and development of novel AMPs. DATA DESCRIPTION: This data note provides two sets of models, as well as two AMP and four non-AMP sequence sets for training and testing the balanced and imbalanced models. Each model set includes five single sub-models that form an ensemble model. The first model set corresponds to the original model trained on a balanced training set that has been described in the original AMPlify manuscript, while the second model set was trained on an imbalanced training set.
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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.