DeepREAD
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
- Shape Therapeutics
- Organisation type
- Industry
- Country
- United States of America
- Published
- 28 September 2024
- Authors
- Yue Jiang, Lina R. Bagepalli, Bora S. Banjanin, Yiannis A. Savva, Yingxin Cao, Lan Guo, Adrian W. Briggs, Brian Booth, Ronald J. Hause
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
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
- 201,012 tokens
112000 gRNAs *113 (sequence length)=12656000
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
- Generative Machine Learning of ADAR Substrates for Precise and Efficient RNA Editing
- Last updated
- 28 November 2025
What the numbers mean
Background
DeepREAD was published by Shape Therapeutics, in United States of America, in September 2024. industry is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
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
It was trained on about 201,012 tokens of text.
Answers
DeepREAD — common questions
How many parameters does DeepREAD have?
No parameter count has been published for DeepREAD, which is why no memory or speed figure appears on this page.
Who created DeepREAD?
DeepREAD was published by Shape Therapeutics, based in United States of America, categorised as industry.
When was DeepREAD released?
DeepREAD 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 DeepREAD used for?
DeepREAD works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run DeepREAD?
None. DeepREAD 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 DeepREAD open source?
The licensing for DeepREAD 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.