Yuel 2
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
- Pennsylvania State University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 12 October 2024
- Authors
- Jian Wang, Nikolay V. Dokholyan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein-ligand 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
Pre-training data = 250,000 protein-ligand pairs 250,000 = 2.5e5 unique data points
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 (non-commercial)
- Training code
- Open (non-commercial)
GNU GENERAL PUBLIC LICENSE Yuel follow GPL 3.0v license. Yuel are freely available for academic purpose or individual research, but restricted for commecial use. https://bitbucket.org/dokhlab/yuel/src/master/
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
- 2
Sources
Where this record came from and when it was last checked.
- Reference
- Leveraging Transfer Learning for Predicting Protein-Small Molecule Interactions
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Yuel 2 was published by Pennsylvania State University, in United States of America, in October 2024. The organisation is categorised as academia.
It works in Biology, and is recorded as doing protein-ligand binding affinity prediction.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
Answers
Yuel 2 — common questions
How many parameters does Yuel 2 have?
No parameter count has been published for Yuel 2, which is why no memory or speed figure appears on this page.
Who created Yuel 2?
Yuel 2 was published by Pennsylvania State University, based in United States of America, categorised as academia.
When was Yuel 2 released?
Yuel 2 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 Yuel 2 used for?
Yuel 2 works in Biology, and is recorded as handling protein-ligand binding affinity 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 Yuel 2?
The weights for Yuel 2 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 Yuel 2?
We cannot say. Yuel 2 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 Yuel 2 open source?
Its weights are published, so Yuel 2 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.