DeepAVPred: A Deep Learning Framework Integrating RECM-DWT and BiGRU for Accurate Antiviral Peptide Prediction

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 AVP_train 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, AVP_test, 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.