AI-Driven Efficient De Novo design of GLP-1RAs with Extended Half-Life and Enhanced Efficacy

Abstract

Peptide drug has revolutionized modern therapeutics, offering novel treatment avenues for various diseases. However, low efficacy, time consumption, and high cost hinder peptide drug design and discovery. We present an efficientin approach that integrates deep learning-based protein design with efficient functional screening, enabling the rapid design of biotechnologically important peptides with improved the stability and efficacy. We designed 10,000 de novo GLP-1 receptor agonists (GLP-1RAs), of which 60 met the stability, efficacy, and diversity criteria in the vitral functional screening. In vitro validation revealed a 62% success rate, while in vivo experiment demonstrated that two designed GLP-1RAs exhibited significantly extended half-lives, approximately three times longer than Semaglutide. In diabetic and obesity mouse models, the most competitive candidate showed superior therapeutic efficacy over Semaglutide. Our AI driven peptide design pipline integrats protein design, functional screening, and experiment validation, reducing the number of iterations required to find novel peptide candidates. The entire process, from design to screening, can be completed in a single cycle within two weeks.