# Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency

*PRI Rank #17 · Topics and Trends in Most Cited Neuropeptides and Animal Physiology Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/neuropeptides-and-animal-physiology/2026/rank-17*

| Field | Value |
| --- | --- |
| Rank | #17 |
| 18m citations | 26 |
| Journal | Nature Chemistry |
| Year | 2024 |
| DOI | 10.1038/s41557-024-01532-x |
| Corresponding authors | Lucy J. Colwell |
| Institution | University of Cambridge, United Kingdom |

**Ranking page:** [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)

**Paper link:** [10.1038/s41557-024-01532-x](https://doi.org/10.1038/s41557-024-01532-x)

## 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

## Cite this ranking

```
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.

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
```