CLEAN-Contact
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
- Cleveland Clinic,Kent State University,Pacific Northwest National Laboratory
- Organisation type
- Academia,Academia,Government
- Country
- United States of America
- Published
- 8 October 2024
- Authors
- Yuxin Yang, Abby Jerger, Song Feng, Zixu Wang, Christina Brasfield, Margaret S. Cheung, Jeremy Zucker, Qiang Guan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Enzyme function 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
- 224,742 tokens
224,742 = 2.24742e5 datapoints (unique protein sequences with contact maps from Swiss-Prot database)
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
- Unreleased
- Training code
- Open (non-commercial)
All codes and data used in training and testing are available at https://github.com/pnnl-predictive-phenomics/clean-contact. ( NON-EXCLUSIVE RESEARCH USE LICENSE FOR CLEAN SOFTWARE) CLEAN-Contact is also freely accessible through an easy-to-use web server: https://ersa.guans.cs.kent.edu/
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
- 1
Sources
Where this record came from and when it was last checked.
- Reference
- CLEAN-Contact: Contrastive Learning-enabled Enzyme Functional Annotation Prediction with Structural Inference
- Last updated
- 28 November 2025
What the numbers mean
About this model
CLEAN-Contact was published by Cleveland Clinic,Kent State University,Pacific Northwest National Laboratory, in United States of America, in October 2024. It comes out of academia,Academia,Government.
It works in Biology, and is recorded as doing enzyme function prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Around 224,742 tokens went into training it.
Answers
CLEAN-Contact — common questions
What is CLEAN-Contact used for?
CLEAN-Contact works in Biology, and is recorded as handling enzyme function prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run CLEAN-Contact?
None. CLEAN-Contact 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 CLEAN-Contact open source?
No. CLEAN-Contact has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does CLEAN-Contact have?
No parameter count has been published for CLEAN-Contact, which is why no memory or speed figure appears on this page.
Who created CLEAN-Contact?
CLEAN-Contact was published by Cleveland Clinic,Kent State University,Pacific Northwest National Laboratory, based in United States of America, categorised as academia,Academia,Government.
When was CLEAN-Contact released?
CLEAN-Contact 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.
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.