ArticleComputers in biology and medicine2019
AntiVPP 1.0: A portable tool for prediction of antiviral peptides.
Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- 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
- Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.Scientific reports · 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
- Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.Briefings in bioinformatics · 2024Review
- DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.Bioinformatics (Oxford, England) · 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
- Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model.BMC bioinformatics · 2024Article
- Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.Current medicinal chemistry · 2024Review
- Analysis, Modeling, and Target-Specific Predictions of Linear Peptides Inhibiting Virus Entry.ACS omega · 2023Article
- VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation.Briefings in bioinformatics · 2023Article
- Combined charge and hydrophobicity-guided screening of antibacterial peptides: two-level approach to predict antibacterial activity and efficacy.Amino acids · 2023Article
- Antiviral Peptides (AVPs) of Marine Origin as Propitious Therapeutic Drug Candidates for the Treatment of Human Viruses.Molecules (Basel, Switzerland) · 2022Review
- Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.Briefings in bioinformatics · 2022Article
- AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation.Bioinformatics advances · 2022Article
- VirVACPRED: A Web Server for Prediction of Protective Viral Antigens.International journal of peptide research and therapeutics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity and constitute excellent alternatives for the treatment of viral infections. In the present study, we developed AntiVPP 1.0, an accurate bioinformatic tool that uses the Random Forest algorithm for antiviral peptide predictions. The model of AntiVPP 1.0 for antiviral peptide predictions uses several features of 1088 peptides for training and validation. During the validation of the model we achieved the TPR = 0.87, SPC = 0.97, ACC = 0.93 and MCC = 0.87 performance measures, which were indicative of a robust model. AntiVPP 1.0 is a fast, accurate and intuitive software focused on the assessment of antiviral peptides candidates. AntiVPP 1.0 is available at https://github.com/bio-coding/AntiVPP.
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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.