MuPIPR
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
- University of California Los Angeles (UCLA),University of Pennsylvania
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 5 March 2020
- Authors
- Guangyu Zhou, Muhao Chen, Chelsea J T Ju, Zheng Wang, Jyun-Yu Jiang, Wei Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein interaction 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
- tokens
STRING Database: 66,235 sequences × 300 amino acids = 19,870,500 tokens SKEMPI Dataset: 5,004 sequences × 300 amino acids = 1,501,200 tokens Total = 19,870,500 + 1,501,200 = 21,371,700 tokens ≈ 2.1 × 10^7
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 65
Sources
Where this record came from and when it was last checked.
- Reference
- Mutation effect estimation on protein–protein interactions using deep contextualized representation learning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
MuPIPR was published by University of California Los Angeles (UCLA),University of Pennsylvania, in the country recorded as United States of America, during March 2020. The category the publisher falls under is academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein interaction prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
MuPIPR — common questions
MuPIPR— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein interaction prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
MuPIPR— 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.
MuPIPR— 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.
MuPIPR— 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.
MuPIPR— who created it?
It was published by University of California Los Angeles (UCLA),University of Pennsylvania, based in United States of America, an organisation categorised as academia,Academia.
MuPIPR— when was it released?
It was published in March 2020. 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.