ArticleBriefings in bioinformatics2024
A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- PMAVP: A Mamba-Inspired Deep Learning Framework for Antiviral Peptide Identification and Functional Activity Prediction.International journal of molecular sciences · 2026Article
- CKAN-ATHP: a predictor for antihypertensive peptides based on sample augmentation and loss improvement strategies using the convolutional Kolmogorov-Arnold network.Journal of cheminformatics · 2026Article
- GRUATT-AVP: leveraging a novel attention-based gated recurrent unit to advance the accuracy of antiviral peptide prediction.Scientific reports · 2025Article
- Article
- AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides.Scientific reports · 2025Article
- A hybrid framework of generative deep learning for antiviral peptide discovery.Scientific reports · 2025Article
- Analysis of In Silico Properties and In Vitro Immunomodulatory Effects of Seven Synthetic Host Defence Peptides in Gilthead Seabream (Sparus aurata) Leucocytes.Marine biotechnology (New York, N.Y.) · 2025Article
- Graph-RPI: predicting RNA-protein interactions via graph autoencoder and self-supervised learning strategies.Briefings in bioinformatics · 2025Article
- AVPpred-BWR: antiviral peptides prediction via biological words representation.Bioinformatics (Oxford, England) · 2025Article
- Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy.Scientific reports · 2025Article
- Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.BMC bioinformatics · 2024Article
- ParaAntiProt provides paratope prediction using antibody and protein language models.Scientific reports · 2024Article
- DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.BMC bioinformatics · 2024Article
- HemoFuse: multi-feature fusion based on multi-head cross-attention for identification of hemolytic peptides.Scientific reports · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.
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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.