ArticleFrontiers in genetics2023
AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Generative Models for the Discovery of Antiviral Peptides Targeting Dengue Virus: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- Engineering host‑defense peptides enhanced by artificial intelligence and nano delivery systems to overcome biofilms and antimicrobial resistance.Engineering microbiology · 2026Review
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.Small science · 2025Article
- Review
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Article
- Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria.F1000Research · 2024Article
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides are present ubiquitously in intra- and extra-biological environments and display considerable antibacterial and antifungal activities. Clinically, it has shown good antibacterial effect in the treatment of diabetic foot and its complications. However, the discovery and screening of antimicrobial peptides primarily rely on wet lab experiments, which are inefficient. This study endeavors to create a precise and efficient method of predicting antimicrobial peptides by incorporating novel machine learning technologies. We proposed a deep learning strategy named AMP-EBiLSTM to accurately predict them, and compared its performance with ensemble learning and baseline models. We utilized Binary Profile Feature (BPF) and Pseudo Amino Acid Composition (PSEAAC) for effective local sequence capture and amino acid information extraction, respectively, in deep learning and ensemble learning. Each model was cross-validated and externally tested independently. The results demonstrate that the Enhanced Bi-directional Long Short-Term Memory (EBiLSTM) deep learning model outperformed others with an accuracy of 92.39% and AUC value of 0.9771 on the test set. On the other hand, the ensemble learning models demonstrated cost-effectiveness in terms of training time on a T4 server equipped with 16 GB of GPU memory and 8 vCPUs, with training durations varying from 0 to 30 s. Therefore, the strategy we propose is expected to predict antimicrobial peptides more accurately in the future.
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