AVP-Pro: An Adaptive Multi-Modal Fusion and Contrastive Learning Approach for Comprehensive Two-Stage Antiviral Peptide Identification

TL;DR

AVP-Pro is a novel two-stage framework that combines adaptive multi-modal feature fusion and contrastive learning to improve antiviral peptide identification accuracy, especially in distinguishing highly similar sequences, with demonstrated superior performance over existing methods.

Contribution

The paper introduces AVP-Pro, a new two-stage predictive framework utilizing hierarchical feature fusion and contrastive learning, advancing antiviral peptide identification accuracy and robustness.

Findings

Achieved 95.31% accuracy and 0.9064 MCC in the first stage.

Effectively classified viral subtypes with high accuracy under small-sample conditions.

Outperformed existing state-of-the-art methods in AVP identification.

Abstract

The accurate identification of antiviral peptides (AVPs) is crucial for novel drug development. However, existing methods still have limitations in capturing complex sequence dependencies and distinguishing confusing samples with high similarity. To address these challenges, we propose AVP-Pro, a novel two-stage predictive framework that integrates adaptive feature fusion and contrastive learning. To comprehensively capture the physicochemical properties and deep-seated patterns of peptide sequences, we constructed a panoramic feature space encompassing 10 distinct descriptors and designed a hierarchical fusion architecture. This architecture integrates self-attention and adaptive gating mechanisms to dynamically modulate the weights of local motifs extracted by CNNs and global dependencies captured by BiLSTMs based on sequence context. Targeting the blurred decision boundary caused by the high similarity between positive and negative sample sequences, we adopted an Online Hard Example Mining (OHEM)-driven contrastive learning strategy enhanced by BLOSUM62. This approach significantly sharpened the model's discriminative power. Model evaluation results show that in the first stage of general AVP identification, the model achieved an accuracy of 0.9531 and an MCC of 0.9064, outperforming existing state-of-the-art (SOTA) methods. In the second stage of functional subtype prediction, combined with a transfer learning strategy, the model realized accurate classification of 6 viral families and 8 specific viruses under small-sample conditions. AVP-Pro provides a powerful and interpretable new tool for the high-throughput screening of antiviral drugs. To further enhance accessibility for users, we have developed a user-friendly web interface, which is available at https://wwwy1031-avp-pro.hf.space.