ArticlePloS one2013
Analysis and prediction of highly effective antiviral peptides based on random forests.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
What it found
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Who cites it
85 citing papers in PubMed.
- Machine Learning in Preclinical Development of Antiviral Peptide Candidates: A Review of the Current Landscape.Viruses · 2026Review
- Genome-Wide Identification of the Transient Receptor Potential Channel Family in Nile Tilapia and Expression Analysis in Response to Cold Stress.Animals : an open access journal from MDPI · 2025Article
- In silico design and validation of a Plasmodium falciparum multi-epitope diagnostic antigen (PfMEDA1) for the diagnosis of falciparum malaria.BMC infectious diseases · 2025Article
- Accurate structure-activity relationship prediction of antioxidant peptides using a multimodal deep learning framework.Journal of cheminformatics · 2025Article
- Next-generation antiviral peptides: AI-driven design, translational delivery platforms, and future therapeutic directions.Virus research · 2025Review
- A Redefined Protocol for Protein Corona Analysis on Graphene Oxide.ACS nanoscience Au · 2025Article
- Immunoinformatic Prediction of HIV-1 Glycoprotein gp120 and Nef Epitopes Conjugated to HBsAg-Binding Protein (SBP) to Induce the Humoral and Cellular Immune Response.International journal of molecular sciences · 2025Article
- Biochemical Characterization of a Novel, Glucose-Tolerant β-Glucosidase from Jiangella ureilytica KC603, and Determination of Resveratrol Production Capacity from Polydatin.Applied biochemistry and biotechnology · 2025Article
- Immunoinformatics study of CD40 ligand-targeting vaccine constructs: a novel immunotherapeutic approach.Osong public health and research perspectives · 2025Article
- Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.Molecular diversity · 2025Review
- A hybrid framework of generative deep learning for antiviral peptide discovery.Scientific reports · 2025Article
- Investigations on genomic, topological and structural properties of diguanylate cyclases involved inCurrent research in structural biology · 2025Article
- Designing a multi-epitope vaccine against African swine fever virus using immunoinformatics approach.Scientific reports · 2025Article
- Targeted deletions in human mitochondrial DNA engineered by Type V CRISPR-Cas12a system.NAR molecular medicine · 2025Article
- AVPpred-BWR: antiviral peptides prediction via biological words representation.Bioinformatics (Oxford, England) · 2025Article
- Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models.Frontiers in veterinary science · 2025Article
- In silico design and assessment of a multi-epitope peptide vaccine against multidrug-resistantIn silico pharmacology · 2025Article
- ToxiPep: Peptide toxicity prediction via fusion of context-aware representation and atomic-level graph.Computational and structural biotechnology journal · 2025Article
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- Roles of Mature Domain Targeting Signals (MTSs) for Protein Translocation and Secretion inInternational journal of molecular sciences · 2024Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficient, which exceeds the previous models. The results suggest that aggregation could be an important feature for identifying antiviral peptides. In addition, our analysis reveals the characteristics of the antiviral peptides such as the importance of lysine and the abundance of α-helical secondary structures.
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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.