EGNN
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
- InstaDeep
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 30 May 2023
- Authors
- Sebastien Boyer, Sam Money-Kyrle, Oliver Bent
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein stability 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
- 420,479 tokens
600000 datapoints
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 5
Sources
Where this record came from and when it was last checked.
- Reference
- Predicting protein stability changes under multiple amino acid substitutions using equivariant graph neural networks
- Last updated
- 25 May 2026
What the numbers mean
About this model
EGNN was published by InstaDeep, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during May 2023. It comes out of an organisation categorised as industry.
It works in the domain of Biology, and is recorded as performing the task of protein stability prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training set ran to roughly 420,479 tokens of text.
Answers
EGNN — common questions
EGNN— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
EGNN— who created it?
It was published by InstaDeep, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
EGNN— when was it released?
It was published in May 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
EGNN— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein stability prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
EGNN— what GPU do I need to run it?
None. This 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.
EGNN— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.