ArticleScientific reports2020
Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 98 citations in OpenAlex.
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- Machine Learning in Preclinical Development of Antiviral Peptide Candidates: A Review of the Current Landscape.Viruses · 2026Review
- GRUATT-AVP: leveraging a novel attention-based gated recurrent unit to advance the accuracy of antiviral peptide prediction.Scientific reports · 2025Article
- Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.Molecular diversity · 2025Review
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- AVPpred-BWR: antiviral peptides prediction via biological words representation.Bioinformatics (Oxford, England) · 2025Article
- Characterization of Novel Plantaricin-Derived Antiviral Peptides Against Flaviviruses.International journal of molecular sciences · 2025Article
- EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.Current medicinal chemistry · 2025Article
- Computational investigation of antiviral peptide interactions with Mpox DNA polymerase.In silico pharmacology · 2025Article
- Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models.Frontiers in veterinary science · 2025Article
- Harnessing Antiviral Peptides: From Molecular Mechanisms to Clinical Translation.Current research in pharmacology and drug discovery · 2025Review
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Role of Peptide Associations in Enhancing the Antimicrobial Activity of Adepantins: Comparative Molecular Dynamics Simulations and Design Assessments.International journal of molecular sciences · 2024Article
- In Vitro Profiling of the Antiviral Peptide TAT-I24.International journal of molecular sciences · 2024Article
- Innovative Alignment-Based Method for Antiviral Peptide Prediction.Antibiotics (Basel, Switzerland) · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
- Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.Molecular diversity · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR .
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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.