Abstract: Antimicrobial peptides, as small molecular peptides with extensive antibacterial activity, have shown great potential in food preservation and other fields because of their unique antibacterial mechanism. However, traditional screening methods are time-consuming and resource-consuming, and often yield antimicrobial peptides with poor stability and high cytotoxicity, limiting their wide application. In recent years, the rapid development of artificial intelligence technology has brought new opportunities for research on antimicrobial peptides. Artificial intelligence algorithms can be continuously optimized based on prior knowledge and real-time data, which significantly improves the prediction efficiency of antibacterial peptides and reduces research and development costs. Additionally, these algorithms offer the possibility to explore the diversity of antimicrobial peptides and optimize their properties. Currently, several specialized databases have been established, providing rich resources for algorithmic model training. Furthermore, multi-source bioinformatics data such as genomics, transcriptomics and proteomics are also widely used to predict antimicrobial peptides, with a view to identifying peptides with potential antimicrobial activity more accurately. This article reviews the principles and applicability of various current artificial intelligence algorithmic models for predicting antimicrobial peptides, and explores prediction models specifically designed to address the dilemma facing the application of antimicrobial peptides. It aims to guide readers in selecting and designing artificial intelligence algorithms and to promote their innovative applications in the fields of food safety and human health.

Key words: artificial intelligence algorithm; machine learning; deep learning; antimicrobial peptides