A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria.

ABSTRACT: The discovery of novel antimicrobial peptides (AMPs) against clinical superbugs is urgently needed to address the ongoing antibiotic resistance crisis. AMPs are promising candidates due to their broad-spectrum activity, rapid bactericidal mechanisms and reduced likelihood of inducing resistance compared with conventional antibiotics. Here, a pre-trained protein large language model (LLM), ProteoGPT, was established and further developed into multiple specialized subLLMs to assemble a sequential pipeline. This pipeline enables rapid screening across hundreds of millions of peptide sequences, ensuring potent antimicrobial activity and minimizing cytotoxic risks. Through transfer learning, we endowed the LLMs with different domain-specific knowledge to achieve high-throughput mining and gener

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12578630 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature