ArticleInternational journal of molecular sciences2023
Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- AFP-GFuse: an antifungal peptide identification model with structural information fusion via multi-graph neural networks and cross-attention mechanism.Molecular diversity · 2026Article
- iALP: Identification of Allergenic Proteins Based on Large Language Model and Gate Linear Unit.Interdisciplinary sciences, computational life sciences · 2025Article
- AVPpred-BWR: antiviral peptides prediction via biological words representation.Bioinformatics (Oxford, England) · 2025Article
- Advances of deep Neural Networks (DNNs) in the development of peptide drugs.Future medicinal chemistry · 2025Review
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Computational Prediction and Structural Analysis of α-Hairpinins, a Ubiquitous Family of Antimicrobial Peptides, Using the Cysmotif Searcher Pipeline.Antibiotics (Basel, Switzerland) · 2024Article
- dsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation.Antibiotics (Basel, Switzerland) · 2024Article
- MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool.Heliyon · 2024Article
- Translocation of Antimicrobial Peptides across Model Membranes: The Role of Peptide Chain Length.Molecular pharmaceutics · 2024Article
- DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.Protein science : a publication of the Protein Society · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.
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Registered trials
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