Artificial intelligence-driven antimicrobial peptide discovery

Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to ized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.

Automated summary

Antimicrobial peptides (AMPs) are promising agents against antimicrobial resistance, and their discovery and generation can be enhanced through discrimination and generation approaches. Recent achievements in AI-driven AMP discovery are reviewed, highlighting exciting directions.