Unlearn and Structure Therapeutics Collaborate on AI-Generated Digital Twins in Obesity Research

SAN FRANCISCO, November 12, 2026 /CourierPR/ -- Unlearn and Structure Therapeutics have joined forces to explore the application of AI-generated digital twins in obesity research. The collaboration aims to analyze existing data from Structure's completed ACCESS open-label extension (OLE) study of aleniglipron, an investigational, once-daily, orally available small molecule agonist of the glucagon-like peptide-1 (GLP-1) receptor.

Unlearn will use AI-based modeling to estimate what participant outcomes might have been without the investigational treatment. These model-generated estimates, referred to as "digital twins," will be compared with the observed study outcomes to provide additional context for interpreting the longer-term findings of the study. This retrospective analysis will be presented at ObesityWeek, The Obesity Society's annual meeting, from November 14-17, 2026, in National Harbor, Maryland.

The ACCESS OLE study involved participants living with overweight or obesity, and the analysis will provide insights into the potential impact of aleniglipron on these individuals. Unlearn's AI-based modeling will help to better understand the long-term effects of the treatment and provide a more comprehensive view of the clinical profile of aleniglipron.

This collaboration between Unlearn and Structure Therapeutics highlights the growing role of artificial intelligence in clinical research and the potential for AI-generated digital twins to enhance the interpretation of complex medical data. The findings from this analysis could have significant implications for the development of new treatments for obesity and related metabolic diseases.