Antimicrobial peptide prediction based on contrastive learning and gated convolutional neural network.

ABSTRACT: The excessive use of antibiotics presents significant risks, as it not only drives the emergence of antibiotic resistance in microbial pathogens but also disrupts the microbial communities essential for maintaining normal human physiological functions. Antimicrobial peptides (AMPs) have garnered increasing attention as a highly promising alternative to antibiotics. The use of computational methods to identify AMPs is becoming increasingly popular, as these approaches can considerably reduce the time and cost involved. In this work, we propose a deep learning-based AMP recognition framework, CG-AMP, aimed at efficiently identifying AMPs. CG-AMP adopts a dual-module architecture, where the first module learns the feature representation space through a pre-trained language model and contrasti

SUBMITTER: Li G 

PROVIDER: S-EPMC12749617 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature