ArticleProtein science : a publication of the Protein Society2023
DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.
Article in Protein science : a publication of the Protein Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 34 citations in OpenAlex.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery.PLoS computational biology · 2026Article
- Harnessing Sequence Embedding and Ensemble Learning to Identify Antifungal Peptides with Low Hemolytic Risk.ACS omega · 2026Article
- AFP-GFuse: an antifungal peptide identification model with structural information fusion via multi-graph neural networks and cross-attention mechanism.Molecular diversity · 2026Article
- Root-associated protein prediction using a protein large language model and hypergraph convolutional networks.Scientific reports · 2026Article
- Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.World journal of microbiology & biotechnology · 2025Review
- PREDAC-FluB: predicting antigenic clusters of seasonal influenza B viruses with protein language model embedding based convolutional neural network.Briefings in bioinformatics · 2025Article
- ConsAMPHemo: A computational framework for predicting hemolysis of antimicrobial peptides based on machine learning approaches.Protein science : a publication of the Protein Society · 2025Article
- Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy.Scientific reports · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.BMC bioinformatics · 2024Article
- DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.BMC bioinformatics · 2024Article
- MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool.Heliyon · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
- ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network.Briefings in bioinformatics · 2024Article
- A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy.Briefings in bioinformatics · 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
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.
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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.