Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency
Most Cited Paper
in Neuropeptides and Animal Physiology
18 Months Post-Publication
Class of 2026
“Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency”
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pri.pepkio.com
Topics
GCGR · GLP-1 receptor · Dual GIP/GLP-1 receptor agonists · peptide sequence optimization · deep multi-task neural network · multiple loss optimization · in vitro potency prediction · Type 2 diabetes mellitus · obesity · model-guided sequence design · glucagon receptor · GLP-1 receptor · biological activity enhancement · sevenfold potency improvement · machine learning peptide design
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Pepkio Research Index (PRI). Topics and Trends in Most Cited Neuropeptides and Animal Physiology Papers, Class of 2026. https://pri.pepkio.com/top-papers/neuropeptides-and-animal-physiology/2026. Accessed 2026-07-31. Methodology Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459