EnOpt
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
- University of Pittsburgh
- Organisation type
- Academia
- Country
- United States of America
- Published
- 5 September 2024
- Authors
- Roshni Bhatt, Ann Wang, Jacob D. Durrant
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Teaching old docks new tricks with machine learning enhanced ensemble docking
- Last updated
- 28 November 2025
What the numbers mean
About this model
EnOpt was published by University of Pittsburgh, in United States of America, in September 2024. It comes out of academia.
It works in Biology, and is recorded as doing protein-ligand binding affinity prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
EnOpt — common questions
Who created EnOpt?
EnOpt was published by University of Pittsburgh, based in United States of America, categorised as academia.
When was EnOpt released?
EnOpt 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 EnOpt used for?
EnOpt works in Biology, and is recorded as handling protein-ligand binding affinity prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run EnOpt?
None. EnOpt 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 EnOpt open source?
The licensing for EnOpt 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 EnOpt have?
No parameter count has been published for EnOpt, which is why no memory or speed figure appears on this page.
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.