ProRNA3D-Single
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Virginia Tech (Virginia Polytechnic Institute and State University)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 28 July 2024
- Authors
- Rahmatullah Roche, Sumit Tarafder, Debswapna Bhattacharya
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding 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
- Epochs
- 100
750 complexes × (1000 protein residues + 250 RNA nucleotides) per complex = 750 × 1250 = 937,500 datapoints ≈ 9.4e5 datapoints
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 A100
- Chips used
- 1
- Power draw
- 433 W
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Unreleased
An open-source software implementation of ProRNA3D-single, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/ProRNA3D-single.
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
- Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
ProRNA3D-Single was published by Virginia Tech (Virginia Polytechnic Institute and State University), in United States of America, in July 2024. It comes out of academia.
It works in Biology, and is recorded as doing protein folding prediction.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Answers
ProRNA3D-Single — common questions
How many parameters does ProRNA3D-Single have?
No parameter count has been published for ProRNA3D-Single, which is why no memory or speed figure appears on this page.
Who created ProRNA3D-Single?
ProRNA3D-Single was published by Virginia Tech (Virginia Polytechnic Institute and State University), based in United States of America, categorised as academia.
When was ProRNA3D-Single released?
ProRNA3D-Single was published in July 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 ProRNA3D-Single used for?
ProRNA3D-Single works in Biology, and is recorded as handling protein folding prediction. 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.
Where can I download ProRNA3D-Single?
The weights for ProRNA3D-Single are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run ProRNA3D-Single?
We cannot say. ProRNA3D-Single has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is ProRNA3D-Single open source?
Its weights are published, so ProRNA3D-Single can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
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.