ProtSSN
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
- Shanghai Jiao Tong University,East China University of Science and Technology,Shanghai AI Lab
- Organisation type
- Academia,Academia,Academia
- Country
- China
- Published
- 6 September 2024
- Authors
- Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design
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.
- Parameters
- 1.5B
- Training data
- tokens
30,948 proteins * 300 amino acids/protein = 9,284,400 tokens ≈ 1.2e7 tokens 30,948 * 300 = 9,284,400
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 A100
- Chips used
- 1
- Power draw
- 433 W
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 7
Sources
Where this record came from and when it was last checked.
- Reference
- Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and Thermostability
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
ProtSSN was published by Shanghai Jiao Tong University,East China University of Science and Technology,Shanghai AI Lab, in China, in September 2024. It comes out of academia,Academia,Academia.
It works in Biology, and is recorded as doing protein design.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
ProtSSN — common questions
Who created ProtSSN?
ProtSSN was published by Shanghai Jiao Tong University,East China University of Science and Technology,Shanghai AI Lab, based in China, categorised as academia,Academia,Academia.
When was ProtSSN released?
ProtSSN 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 ProtSSN used for?
ProtSSN works in Biology, and is recorded as handling protein design. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run ProtSSN?
None. ProtSSN 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 ProtSSN open source?
The licensing for ProtSSN 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 ProtSSN have?
ProtSSN has 1.5B parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.