AlphaMissense
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 22 September 2023
- Authors
- Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvile ̇Žemgulyte ̇, Taylor Applebaum, Alexander Pritzel, Lai Hong Wong, Michal Zielinski, Tobias Sargeant, Rosalia G. Schneider,Andrew W. Senior, John Jumper, Demis Hassabis, Pushmeet Kohli,Žiga Avsec
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein pathogenicity prediction, Protein folding prediction, Proteins
- Base model
- AlphaFold 2
- Numerical format
- BF16
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.
- Parameters
- 93M
- Training data
- 2,304,000,000 tokens
- Epochs
- 4
"The model architecture is similar to that of AlphaFold (21), with minor modifications" Reference is to the AlphaFold 2 paper; that model had 93 million parameters
7800000 samples - size of training dataset (see Table S4 in supplementary materials) +1,345,605 variants for fine-tuning (but less could be used) see Table S1 around 9000000 samples is quite confident estimation
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 source
Apache for code. weights not released https://github.com/google-deepmind/alphamissense
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Likely
- Citations
- 1,226
"By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data." [Abstract]
Sources
Where this record came from and when it was last checked.
- Reference
- Accurate proteome-wide missense variant effect prediction with AlphaMissense
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
AlphaMissense was published by Google DeepMind, in United States of America, in September 2023. industry is the category the publisher falls under.
It works in Biology, and is recorded as doing protein pathogenicity prediction, Protein folding prediction, Proteins.
It builds on AlphaFold 2, which is why it shares that model's general shape and size.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Around 2,304,000,000 tokens went into training it.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
AlphaMissense — common questions
When was AlphaMissense released?
AlphaMissense was published in September 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.
What is AlphaMissense used for?
AlphaMissense works in Biology, and is recorded as handling protein pathogenicity prediction, Protein folding prediction, Proteins. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run AlphaMissense?
None. AlphaMissense 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 AlphaMissense open source?
No. AlphaMissense has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AlphaMissense have?
AlphaMissense has 93M parameters. "The model architecture is similar to that of AlphaFold (21), with minor modifications" Reference is to the AlphaFold 2 paper; that model had 93 million parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created AlphaMissense?
AlphaMissense was published by Google DeepMind, based in United States of America, categorised as industry.
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.