SAAMBE-MEM
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
- Clemson University,Central China Normal University
- Organisation type
- Academia,Academia
- Country
- United States of America, China
- Published
- 6 September 2024
- Authors
- Prawin Rimal, Shailesh Kumar Panday, Wang Xu, Yunhui Peng, Emil Alexov
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Mutation prediction, Protein protein binding affinity 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
Dataset: MPAD_Clean Unique datapoints = 1040 mutations = 1.04e3 Calculations: 1040 = 1.04 × 10³ Final result: 1.04e3 datapoints
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
- Hosted access (no API)
- Training code
- Unreleased
The method is accessible via a web server and standalone code at http://compbio.clemson.edu/SAAMBE-MEM/
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
- SAAMBE-MEM: A Sequence-Based Method for Predicting Binding Free Energy Change upon Mutation in Membrane Protein-Protein Complexes
- Last updated
- 28 November 2025
What the numbers mean
About this model
SAAMBE-MEM was published by Clemson University,Central China Normal University, in United States of America, in September 2024. The organisation is categorised as academia,Academia.
It works in Biology, and is recorded as doing mutation prediction, Protein protein binding affinity prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
SAAMBE-MEM — common questions
What GPU do I need to run SAAMBE-MEM?
None. SAAMBE-MEM 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 SAAMBE-MEM open source?
No. SAAMBE-MEM has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does SAAMBE-MEM have?
No parameter count has been published for SAAMBE-MEM, which is why no memory or speed figure appears on this page.
Who created SAAMBE-MEM?
SAAMBE-MEM was published by Clemson University,Central China Normal University, based in United States of America, categorised as academia,Academia.
When was SAAMBE-MEM released?
SAAMBE-MEM 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 SAAMBE-MEM used for?
SAAMBE-MEM works in Biology, and is recorded as handling mutation prediction, Protein protein binding affinity prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.