Recent Advances in Machine Learning Models for Antiviral Peptide Prediction
DOI:
https://doi.org/10.21015/vtcs.v13i1.2047Abstract
Viral diseases are widespread, and their impact is expressed in millions of cases of infection and mortality around the world. Chronic viral diseases include COVID-19, HIV, and hepatitis. To prevent and treat these viral infections, novel agents and antiviral peptides (AVPs) have been developed. Thus, identifying AVPs is crucial because these pieces of information are invaluable throughout the entire pharmaceutical industry and other sciences. This has been done experimentally and computationally, but the need for better, more accurate, and efficient predictors persists. This research also reviews current AVP predictors, including the datasets employed, feature representation methods, classification algorithms, and assessment criteria. In our paper, we discuss the weaknesses of the existing techniques, overview the most efficient strategies, and evaluate the benefits and drawbacks of the classifiers. Furthermore, several directions for future work are discussed in detail, including enhanced feature representation techniques, feature selection, and classification approaches. The following advancements aim to improve the effectiveness of algorithms for predicting AVPs, enabling the development of new antiviral drugs more successfully.
References
M. Nawaz, H. Yu, F. Akhtar, T. Ma, and H. Zheng, “Deep learning in the discovery of antiviral peptides and peptidomimetics: Databases and prediction tools,” Molecular Diversity, pp. 1–36, 2025.
N. Lefin, L. Herrera-Belén, J. G. Farias, and J. F. Beltrán, “Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides,” Molecular Diversity, vol. 28, no. 4, pp. 2365–2374, 2024.
M. M. A. Shirazi, S. Haghighat, Z. Nikbakht, E. Salimkia, and A. Kiumarsy, “Next-generation antiviral peptides: AI-driven design, translational delivery platforms, and future therapeutic directions,” Virus Research, p. 199642, 2025.
N. Periwal et al., “Antiprotozoal peptide prediction using machine learning with effective feature selection techniques,” Heliyon, vol. 10, no. 16, 2024.
J. Yan, J. Cai, B. Zhang, Y. Wang, D. F. Wong, and S. W. Siu, “Recent progress in the discovery and design of antimicrobial peptides using traditional machine learning and deep learning,” Antibiotics, vol. 11, no. 10, p. 1451, 2022.
M. Daniyal, M. Qureshi, R. R. Marzo, M. Aljuaid, and D. Shahid, “Exploring clinical specialists’ perspectives on the future role of AI: Evaluating replacement perceptions, benefits, and drawbacks,” BMC Health Services Research, vol. 24, no. 1, p. 587, 2024, doi: 10.1186/s12913-024-10848-7.
T. Talaei Khoei and N. Kaabouch, “Machine learning: Models, challenges, and research directions,” Future Internet, vol. 15, no. 10, p. 332, 2023.
G. Varoquaux and O. Colliot, “Evaluating machine learning models and their diagnostic value,” in Machine Learning for Brain Disorders. Cham, Switzerland: Springer, 2023, pp. 601–630.
S. Tufail, H. Riggs, M. Tariq, and A. I. Sarwat, “Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms,” Electronics, vol. 12, no. 8, p. 1789, 2023.
S. Surana et al., “PandoraGAN: Generating antiviral peptides using generative adversarial networks,” SN Computer Science, vol. 4, no. 5, p. 607, 2023.
F. Ali et al., “DBP-DeepCNN: Prediction of DNA-binding proteins using wavelet-based denoising and deep learning,” Chemometrics and Intelligent Laboratory Systems, vol. 229, p. 104639, 2022.
D. Pandey, K. Niwaria, and B. Chourasia, “Machine learning algorithms: A review,” Machine Learning, vol. 6, no. 2, 2019.
J. F. B. Lissabet, L. H. Belén, and J. G. Farias, “AntiVPP 1.0: A portable tool for prediction of antiviral peptides,” Computers in Biology and Medicine, vol. 107, pp. 127–130, 2019.
A. S. Chowdhury et al., “Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance,” Scientific Reports, vol. 10, no. 1, p. 19260, 2020.
T.-T. Lin et al., “AI4AVP: An antiviral peptide predictor using deep learning with generative adversarial network data augmentation,” Bioinformatics Advances, vol. 2, no. 1, p. vbac080, 2022.
M. Qureshi, H. Iftikhar, and M. Daniyal, “Clinical application of machine learning models for early-stage chronic kidney disease detection,” Diagnostics, vol. 15, no. 20, p. 2610, 2025.
S. Gupta et al., “IL17eScan: A tool for the identification of peptides inducing IL-17 response,” Frontiers in Immunology, vol. 8, p. 1430, 2017.
Z. Chen et al., “iFeature: A Python package and web server for feature extraction and selection from protein and peptide sequences,” Bioinformatics, vol. 34, no. 14, pp. 2499–2502, 2018.
Z. N. K. Swati et al., “Content-based brain tumor retrieval for MR images using transfer learning,” IEEE Access, vol. 7, pp. 17809–17822, 2019.
F. Ali et al., “DBPPred-PDSD: A machine learning approach for prediction of DNA-binding proteins using discrete wavelet transform,” Chemometrics and Intelligent Laboratory Systems, vol. 182, pp. 21–30, 2018.
Y. Xu et al., “iSNO-PseAAC: Predicting cysteine S-nitrosylation sites in proteins,” PLoS ONE, vol. 8, no. 2, p. e55844, 2013.
A. N. Sarangi, M. Lohani, and R. Aggarwal, “Prediction of essential proteins in prokaryotes using Chou’s pseudo amino acid composition,” Protein and Peptide Letters, vol. 20, no. 7, pp. 781–795, 2013.
S. Ahmed et al., “Improving secretory protein prediction in Mycobacterium tuberculosis using unbiased dipeptide composition with SVM,” International Journal of Data Mining and Bioinformatics, vol. 21, no. 3, pp. 212–229, 2018.
K.-C. Chou, “Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes,” Bioinformatics, vol. 21, no. 1, pp. 10–19, 2005.
K.-C. Chou, “Prediction of protein cellular attributes using pseudo-amino acid composition,” Proteins: Structure, Function, and Bioinformatics, vol. 43, no. 3, pp. 246–255, 2001.
M. Arif et al., “TargetCPP: Accurate prediction of cell-penetrating peptides using optimized multiscale features and gradient boosting,” Journal of Computer-Aided Molecular Design, vol. 34, pp. 841–856, 2020.
Y.-L. Chen and Q.-Z. Li, “Prediction of apoptosis protein subcellular localization using hybrid pseudo-amino acid composition,” Journal of Theoretical Biology, vol. 248, no. 2, pp. 377–381, 2007.
X.-Y. Sun et al., “Identifying protein quaternary structural attributes using Chou’s PseAAC and discrete wavelet transform,” Molecular BioSystems, vol. 8, no. 12, pp. 3178–3184, 2012.
M. Qureshi, K. Ishaq, M. Daniyal, H. Iftikhar, M. Z. Rehman, et al., “Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan,” BMC Public Health, vol. 25, no. 1, p. 34, 2025, doi: 10.1186/s12889-024-19505-1.
G. Varoquaux and O. Colliot, “Evaluating machine learning models and their diagnostic value,” in Machine Learning for Brain Disorders. Cham, Switzerland: Springer, 2023, pp. 601–630.
S. Tufail, H. Riggs, M. Tariq, and A. I. Sarwat, “Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms,” Electronics, vol. 12, no. 8, p. 1789, 2023.
G. Varoquaux and O. Colliot, “Evaluating machine learning models and their diagnostic value,” in Machine Learning for Brain Disorders. Cham, Switzerland: Springer, 2023, pp. 601–630.
J. M. Kernbach and V. E. Staartjes, “Foundations of machine learning-based clinical prediction modeling: Part II—Generalization and overfitting,” in Machine Learning in Clinical Neuroscience: Foundations and Applications. Cham, Switzerland: Springer, 2021, pp. 15–21.
A. Khan, M. Qureshi, M. Daniyal, and K. Tawiah, “A novel study on machine learning algorithm-based cardiovascular disease prediction,” Health & Social Care in the Community, vol. 31, no. 1, p. 1406060, 2023, doi: 10.1111/hsc.1406060.
I. Hussain, M. Qureshi, M. Ismail, H. Iftikhar, J. Zywiołek, and J. L. López-Gonzales, “Optimal feature selection in high-dimensional data based on robust techniques: Application to different health databases,” Heliyon, vol. 10, no. 17, p. e29564, 2024, doi: 10.1016/j.heliyon.2024.e29564.
A. W. K. Yeung, S. More, J. Wu, and S. B. Eickhoff, “Reporting details of neuroimaging studies on individual traits prediction: A literature survey,” NeuroImage, vol. 256, p. 119275, 2022.
R. Jiang, C. W. Woo, S. Qi, J. Wu, and J. Sui, “Interpreting brain biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging,” IEEE Signal Processing Magazine, vol. 39, no. 4, pp. 107–118, 2022.
R. Wang, P. Chaudhari, and C. Davatzikos, “Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 6, p. e2211613120, 2023.
O. Benkarim et al., “Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging,” PLoS Biology, vol. 20, no. 4, p. e3001627, 2022.
D. Chyzhyk, G. Varoquaux, M. Milham, and B. Thirion, “How to remove or control confounds in predictive models, with applications to brain biomarkers,” GigaScience, vol. 11, p. giac014, 2022.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
