EvolMPNN
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
- Aarhus university
- Organisation type
- Academia
- Country
- Denmark
- Published
- 22 August 2024
- Authors
- Zhiqiang Zhong, Davide Mottin
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Mutation prediction, Protein property 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
- 63,001 tokens
Total proteins = 82,583 + 8,733 + 54,025 = 145,341 proteins Residues per protein (avg) = 200 Total datapoints = 145,341 × 200 = 29,068,200 ≈ 2.9 × 10^7
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open (non-commercial)
no clear license https://github.com/zhiqiangzhongddu/EvolMPNN the repo seems to include training and inference code, I am not sure about model weights
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
- Efficiently Predicting Mutational Effect on Homologous Proteins by Evolution Encoding
- Last updated
- 28 November 2025
What the numbers mean
About this model
EvolMPNN was published by Aarhus university, in Denmark, in August 2024. It comes out of academia.
It works in Biology, and is recorded as doing mutation prediction, Protein property prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training set ran to roughly 63,001 tokens.
Answers
EvolMPNN — common questions
Is EvolMPNN open source?
No. EvolMPNN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does EvolMPNN have?
No parameter count has been published for EvolMPNN, which is why no memory or speed figure appears on this page.
Who created EvolMPNN?
EvolMPNN was published by Aarhus university, based in Denmark, categorised as academia.
When was EvolMPNN released?
EvolMPNN was published in August 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 EvolMPNN used for?
EvolMPNN works in Biology, and is recorded as handling mutation prediction, Protein property 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 EvolMPNN?
None. EvolMPNN 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.
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.