MsPBRsP
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
- Zhengzhou University
- Organisation type
- Academia
- Country
- China
- Published
- 27 February 2023
- Authors
- Yuguang Li, Shuai Lu, Xiaofei Nan, Shoutao Zhang, Qinglei Zhou
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
Total Residues: 2,068,793 Training Set (90%): 0.9 × 2,068,793 = 1,861,913 residues Final Data Amount: 1.86M residues
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- How it was established
- Hardware
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce RTX 2080
- Chips used
- 1
- Power draw
- 236 W
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
- MsPBRsP: Multi-scale Protein Binding Residues Prediction Using Language Model
- Last updated
- 28 November 2025
What the numbers mean
Background
MsPBRsP was published by Zhengzhou University, in the country recorded as China, during February 2023. The publishing organisation is categorised as 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
MsPBRsP — common questions
MsPBRsP— who created it?
It was published by Zhengzhou University, based in China, an organisation categorised as academia.
MsPBRsP— when was it released?
It was published in February 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.
MsPBRsP— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein interaction prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MsPBRsP— 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.
MsPBRsP— 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.
MsPBRsP— 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.
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.