ChemNet
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Washington
- Organisation type
- Academia
- Country
- United States of America
- Published
- 25 September 2024
- Authors
- Ivan Anishchenko, Yakov Kipnis, Indrek Kalvet, Guangfeng Zhou, Rohith Krishna, Samuel J Pellock, Anna Lauko, Gyu Rie Lee, Linna An, Justas Dauparas, Frank DiMaio, David Baker
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 172,062,700 tokens
112,828 data points Calculation: Single dataset (PDB) with 112,828 examples = 112,828 total data points
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Modeling protein-small molecule conformational ensembles with ChemNet
- Last updated
- 28 November 2025
What the numbers mean
About this model
ChemNet was published by University of Washington, in United States of America, in September 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein folding prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Around 172,062,700 tokens went into training it.
Answers
ChemNet — common questions
Who created ChemNet?
ChemNet was published by University of Washington, based in United States of America, categorised as academia.
When was ChemNet released?
ChemNet was published in September 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is ChemNet used for?
ChemNet works in Biology, and is recorded as handling protein folding prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run ChemNet?
None. ChemNet is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is ChemNet open source?
The licensing for ChemNet was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does ChemNet have?
No parameter count has been published for ChemNet, which is why no memory or speed figure appears on this page.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.