Blood-sugar-regulating peptide (CHEMBL1222083)
An experimental short peptide that switches on the body's blood-sugar control system, not an approved drug.
A researcher, an agent, or an algorithm wrote down the sequence and picked a target to hit.
An AI model like OpenFold3 or AlphaFold built a 3D structure and scored how well it fits the binding site.
A second contributor repeated the computation on their own hardware and the scores matched.
A chemistry service or a researcher ordered the sequence, it was manufactured, and mass spectrometry confirmed the right molecule was produced.
A binding or activity measurement confirmed that it actually does what the computer predicted — or didn't.
Research directions for this peptide, selected from the current sources — hypotheses you can explore and model. None of it is proven yet; tap any one to see the full thinking.
Could this peptide do two jobs at the same time, helping the body burn more fat while also controlling blood sugar?
Drugs that activate both the GLP-1 and glucagon pathways are being studied for obesity and fatty liver disease because the combination could burn more energy than either target alone. If this hypothesis holds, a peptide already on the radar for blood sugar control might turn out to be useful for those harder-to-treat conditions too.
Is there a specific spot on this peptide where attaching a fat-like molecule could make it last much longer in the body, the same way it was done to create semaglutide?
Most peptides break down in the bloodstream within hours, which means daily injections. If attaching a fatty acid at the right spot here extends the lifetime to a day or more, it could open a path to a once-weekly GLP-1 drug with a structure different enough from semaglutide to stand on its own scientifically and commercially.
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| EC50 | 0.58 nM | GPCRDB/ChEMBL |
▸structural qualityopenfold3
| metric | value | note |
|---|---|---|
| gpde | 0.795 | global PDE — lower = better |
| disorder | NaN | fraction disordered |
▸3-letter notation
▸recipeboltz-2 1.0
| parameter | value |
|---|---|
| model | boltz-2 1.0 |
| weights | — |
| hardware | nvidia_nim_api |
| mlx version | — |
| python | — |
| random seed | — |
| msa strategy | none |
| diffusion samples | 1 |
| runtime | — |
| predicted by | mlx@peptide |
| predicted at | 2026-04-24 |
▸citationbibtex
@peptide{pep10352,
sequence = {QGTFTSDYSKYLDGRRAQDFVQWLMNT},
target = {glp-1r},
author = {peptidemodel},
year = {2026},
status = {bioassayed}
}