PROPERMAB
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
- Regeneron
- Organisation type
- Industry
- Country
- United States of America
- Published
- 12 October 2024
- Authors
- Bian Li, Shukun Luo, Wenhua Wang, Jiahui Xu, Dingjiang Liu, Mohammed Shameem, John Mattila, Matthew Franklin, Peter G. Hawkins, Gurinder S. Atwal
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Antibody property 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
Feature Prediction: 12,000 mAbs HIC Retention Time: 135 mAbs Viscosity Prediction: 60 mAbs Total = 12,000 + 135 + 60 = 12,195 mAbs (1.2e4)
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
- 4
Sources
Where this record came from and when it was last checked.
- Reference
- PROPERMAB: an integrative framework for in silico prediction of antibody developability using machine learning
- Last updated
- 1 December 2025
What the numbers mean
Where it came from
PROPERMAB was published by Regeneron, in United States of America, in October 2024. It comes out of industry.
It works in Biology, and is recorded as doing antibody property prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
PROPERMAB — common questions
Who created PROPERMAB?
PROPERMAB was published by Regeneron, based in United States of America, categorised as industry.
When was PROPERMAB released?
PROPERMAB was published in October 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 PROPERMAB used for?
PROPERMAB works in Biology, and is recorded as handling antibody property prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run PROPERMAB?
None. PROPERMAB 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 PROPERMAB open source?
The licensing for PROPERMAB was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does PROPERMAB have?
No parameter count has been published for PROPERMAB, 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.