Research Advance in the Development of Antimicrobial Peptides Using Deep Learning

As the global issue of antibiotic resistance becomes increasingly severe, antimicrobial peptides (AMPs), a class of short-chain peptides with broad-spectrum antibacterial activity, have garnered significant attention from the scientific community due to their unique antibacterial properties and potential clinical applications. However, traditional methods for discovering and designing AMPs often rely on repetitive laboratory trials and error corrections, which are not only costly but also inefficient. In contrast, the application of artificial intelligence (AI) technology, particularly deep learning algorithms, for screening and predicting AMPs has demonstrated substantial advantages. Deep learning models can automatically learn and extract key features of AMPs from large-scale datasets, enabling efficient prediction of potential AMP sequences. This approach not only significantly enhances the screening efficiency of AMPs but also reduces research and development costs, thereby opening new avenues for the study and application of AMPs. Therefore, this article provides an overview of the workflow and research progress in utilizing deep learning to predict AMP sequences. The limitations and challenges faced by this technology in the field of AMP prediction are also discussed in this review.