The global rise of antimicrobial resistance (AMR) has emerged as one of the most pressing threats to public health. This crisis calls for the urgent development of alternative therapeutic agents against antibiotic-resistant pathogens. Antimicrobial peptides (AMPs) are widely present in nature, with a broad range of effects and a low risk of causing drug resistance. Therefore, they are an ideal choice for the development of the next generation of antimicrobial drugs. To overcome the inefficiencies of traditional AMP discovery, artificial intelligence (AI) and machine learning (ML) technologies have been increasingly used to predict and design AMPs. Multiple AMP databases were used to train ML models for predicting the activity of AMPs or generating AMP sequences. This review briefly provides a comprehensive overview of AMP databases and computational tools, highlighting their capabilities and challenges. Future work should integrate larger datasets and experimental validation to accelerate clinical translation.
Zhou et al. (Mon,) studied this question.