vScreenML 2.0
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
- Fox Chase Cancer Center,Temple University School of Pharmacy
- Organisation type
- Academia
- Country
- United States of America
- Published
- 12 October 2024
- Authors
- Grigorii V. Andrianov, Emeline Haroldsen, John Karanicolas
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
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
"Thus, our set of actives comprised 1,806 instances, of which 1,321 are unique protein-ligand complexes. Each structure was minimized using PyRosetta prior to calculating vScreenML 2.0 features. From each of the 1,806 actives, 3 property-matched decoys were also generated" Binary classification task between proteins and decoys. Total gradients: 4*1806 = 7224
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
- Unreleased
MIT license for what seems to be inference code only https://github.com/gandrianov/vScreenML2
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
- vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
vScreenML 2.0 was published by Fox Chase Cancer Center,Temple University School of Pharmacy, in the country recorded as United States of America, during October 2024. The publishing organisation is categorised as academia.
It works in the domain of Biology, and is recorded as performing the task of drug discovery.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
vScreenML 2.0 — common questions
vScreenML 2.0— who created it?
It was published by Fox Chase Cancer Center,Temple University School of Pharmacy, based in United States of America, an organisation categorised as academia.
vScreenML 2.0— when was it released?
It 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.
vScreenML 2.0— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of drug discovery. These are the areas it was designed around; they describe intent rather than a hard boundary.
vScreenML 2.0— what GPU do I need to run it?
None. This 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.
vScreenML 2.0— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
vScreenML 2.0— how many parameters does it have?
No parameter count has been published for it, 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.