ArticlePeerJ2024
deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model.
Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 citing papers in PubMed.
- Rational Design and In Silico Evaluation of a Dual-Action Antimicrobial Peptide Targeting Efflux Pump and Membrane Integrity in Pseudomonas aeruginosa.Probiotics and antimicrobial proteins · 2026Article
- Overcoming LPS-mediated resistance in gram-negative pathogens: a review of LL-37 analogs and computational design strategies.Archives of microbiology · 2026Review
- Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.BMC microbiology · 2026Article
- A dual diffusion model-based representation learning framework for antimicrobial peptides classification.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence as a Catalyst for Antimicrobial Discovery: From Predictive Models to De Novo Design.Microorganisms · 2026Review
- Graph-Contrastive Convolutional Neural Network for Extracting and Classifying Peptide-Based Periodontal Immunomodulatory and Anti-Inflammatory Signatures.International dental journal · 2026Article
- Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures.Antibiotics (Basel, Switzerland) · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.World journal of microbiology & biotechnology · 2025Review
- In Silico Identification and Molecular Characterization ofAntibiotics (Basel, Switzerland) · 2025Article
- Article
- Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.Small science · 2025Article
- Antimicrobial peptides: from discovery to developmental applications.Applied and environmental microbiology · 2025Review
- Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.Journal of chemical information and modeling · 2025Article
- Article
- Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models.Frontiers in veterinary science · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Background: Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods: In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps. Results: In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion: deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.
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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.