DeepAVPred: A Deep Learning Framework Integrating RECM-DWT and BiGRU for Accurate Antiviral Peptide Prediction
- 1School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- 2School of Computer Science and Technology, Aba Teachers College, Aba 623002, China
Abstract
Viral infections continue to pose a significant global health challenge. Antiviral peptides (AVPs) have emerged as promising therapeutic agents owing to their high specificity, favorable safety profiles, and broad-spectrum antiviral activity. However, experimental AVP identification is time-consuming and costly, while existing computational predictors often have limitations in predictive accuracy and feature representation. Here, we propose DeepAVPred, a deep learning-based framework for AVP identification. DeepAVPred integrates complementary sequence descriptors to capture physicochemical properties and pairwise residue interactions. We present a novel feature representation, the Residue Energy Content Matrix augmented with a Discrete Wavelet Transform (RECM-DWT). RECM-DWT characterizes multi-scale patterns in residue-energy information and complements conventional sequence descriptors by providing wavelet-derived features across multiple resolution levels. The integrated features are then modeled using an attention-based Bidirectional Gated Recurrent Unit (BiGRU) network to learn contextual dependencies and informative sequence patterns. On the AVPtrain dataset, DeepAVPred achieved strong performance in 10-fold cross-validation, with an accuracy of 0.990, a Matthews Correlation Coefficient (MCC) of 0.980, and an area under the receiver operating characteristic curve (AUC) of 0.993. On the independent test dataset, AVPtest, DeepAVPred achieved an accuracy of 0.942, an MCC of 0.886, and an AUC of 0.949. These results demonstrate the predictive performance of DeepAVPred and support its potential as a computational framework for prioritizing candidate antiviral peptides for further experimental validation.
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BibTeX
@article{fida2026deepavpred,
title = {{DeepAVPred: A Deep Learning Framework Integrating RECM-DWT and BiGRU for Accurate Antiviral Peptide Prediction}},
author = {Huma Fida and Hong-Qi Zhang and Basharat Ahmad and Hao Lin and Cai-Yi Ma and Cheng-Bing Huang and Zhi-Yu Fan},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27209086},
url = {https://www.mdpi.com/1422-0067/27/20/9086},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - DeepAVPred: A Deep Learning Framework Integrating RECM-DWT and BiGRU for Accurate Antiviral Peptide Prediction
AU - Huma Fida
AU - Hong-Qi Zhang
AU - Basharat Ahmad
AU - Hao Lin
AU - Cai-Yi Ma
AU - Cheng-Bing Huang
AU - Zhi-Yu Fan
PY - 2026
DA - 2026/10/13
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27209086
UR - https://www.mdpi.com/1422-0067/27/20/9086
AB - Viral infections continue to pose a significant global health challenge. Antiviral peptides (AVPs) have emerged as promising therapeutic agents owing to their high specificity, favorable safety profiles, and broad-spectrum antiviral activity. However, experimental AVP identification is time-consuming and costly, while existing computational predictors often have limitations in predictive accuracy and feature representation. Here, we propose DeepAVPred, a deep learning-based framework for AVP identification. DeepAVPred integrates complementary sequence descriptors to capture physicochemical properties and pairwise residue interactions. We present a novel feature representation, the Residue Energy Content Matrix augmented with a Discrete Wavelet Transform (RECM-DWT). RECM-DWT characterizes multi-scale patterns in residue-energy information and complements conventional sequence descriptors by providing wavelet-derived features across multiple resolution levels. The integrated features are then modeled using an attention-based Bidirectional Gated Recurrent Unit (BiGRU) network to learn contextual dependencies and informative sequence patterns. On the AVPtrain dataset, DeepAVPred achieved strong performance in 10-fold cross-validation, with an accuracy of 0.990, a Matthews Correlation Coefficient (MCC) of 0.980, and an area under the receiver operating characteristic curve (AUC) of 0.993. On the independent test dataset, AVPtest, DeepAVPred achieved an accuracy of 0.942, an MCC of 0.886, and an AUC of 0.949. These results demonstrate the predictive performance of DeepAVPred and support its potential as a computational framework for prioritizing candidate antiviral peptides for further experimental validation.
SN - 1422-0067
ER -