ArticleInternational journal of molecular sciences2019
Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
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Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Generative Models for the Discovery of Antiviral Peptides Targeting Dengue Virus: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- GANs in Peptide Drug Discovery: From De Novo Design to Multi-Property Optimization and Clinical Translation.Probiotics and antimicrobial proteins · 2026Review
- PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling.PLoS computational biology · 2026Article
- Article
- Machine Learning in Preclinical Development of Antiviral Peptide Candidates: A Review of the Current Landscape.Viruses · 2026Review
- Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures.Antibiotics (Basel, Switzerland) · 2026Article
- In Silico Identification of Antiviral Peptides as Potential Leads Against Sudan Ebolavirus VP-40.BioMed research international · 2026Article
- GRUATT-AVP: leveraging a novel attention-based gated recurrent unit to advance the accuracy of antiviral peptide prediction.Scientific reports · 2025Article
- Design of Hybrid Bioactive Peptides Derived From Cecropin and Cathepsin for Therapeutic Application.Chemical biology & drug design · 2025Article
- A hybrid framework of generative deep learning for antiviral peptide discovery.Scientific reports · 2025Article
- iDNS3IP: Identification and Characterization of HCV NS3 Protease Inhibitory Peptides.International journal of molecular sciences · 2025Article
- VITALdb: to select the best viroinformatics tools for a desired virus or application.Briefings in bioinformatics · 2025Article
- EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.Current medicinal chemistry · 2025Article
- Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models.Frontiers in veterinary science · 2025Article
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025Review
- Role of Peptide Associations in Enhancing the Antimicrobial Activity of Adepantins: Comparative Molecular Dynamics Simulations and Design Assessments.International journal of molecular sciences · 2024Article
- Antimicrobial Peptide with a Bent Helix Motif Identified in Parasitic FlatwormInternational journal of molecular sciences · 2024Article
- Innovative Alignment-Based Method for Antiviral Peptide Prediction.Antibiotics (Basel, Switzerland) · 2024Article
- Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.Molecular diversity · 2024Review
6 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.
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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.