ProtENN2
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
- European Bioinformatics Institute,University of Cambridge,Google Research
- Organisation type
- Research collective,Academia,Industry
- Country
- Multinational, United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 18 September 2024
- Authors
- Irina Ponamareva, Antonina Andreeva, Maxwell L Bileschi, Lucy Colwell, Alex Bateman
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein classification
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
- tokens
Total datapoints = 23,910,108,270 residues = 2.391 × 10¹⁰ Calculation: Single value from training database, no additions or multiplications needed.
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
- Unreleased
Apache 2.0 for code (I suppose it is inference code only, I don't see pre-training code): https://github.com/iponamareva/ProtCNNSim CC BY 4.0 https://zenodo.org/records/10091910
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
- Investigation of protein family relationships with deep learning
- Last updated
- 28 November 2025
What the numbers mean
Background
ProtENN2 was published by European Bioinformatics Institute,University of Cambridge,Google Research, in Multinational, in September 2024. It comes out of research collective,Academia,Industry.
It works in Biology, and is recorded as doing protein classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
ProtENN2 — common questions
What is ProtENN2 used for?
ProtENN2 works in Biology, and is recorded as handling protein classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run ProtENN2?
None. ProtENN2 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 ProtENN2 open source?
No. ProtENN2 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does ProtENN2 have?
No parameter count has been published for ProtENN2, which is why no memory or speed figure appears on this page.
Who created ProtENN2?
ProtENN2 was published by European Bioinformatics Institute,University of Cambridge,Google Research, based in Multinational, categorised as research collective,Academia,Industry.
When was ProtENN2 released?
ProtENN2 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.
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.