A Dual-Modal Contrastive Learning Framework for Antimicrobial Peptides Classification

The increasing prevalence of antibiotic-resistant bacteria has made novel antimicrobial agents greatly needed. Antimicrobial peptides (AMPs) have emerged as promising alternatives, but their identification and classification remain challenging. In this paper, we propose a dual-modal contrastive learning framework for the task of AMPs classification, in which both sequence and structural information of peptides are fully used and effectively integrated. Specifically, a multi-view data processing module aims to encode peptides from multiple perspectives, deriving initial sequence-based features and structure-based features. For better feature learning, a Transformer architecture is conducted on sequence-based features, while a Graph Attention Network (GAT) module is conducted on structure-based features. Moreover, we introduce a dual-modal contrastive learning strategy to enhance the model’s ability for discriminating AMPs and non-AMPs, and to align semantics contained in different modalities. We conduct comprehensive experiments and the results demonstrate the effectiveness of our method on the task of AMPs classification.