CrossBind
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
- Shanghai AI Lab,Fudan University,Loughborough University,Chinese University of Hong Kong (CUHK),Shanghai Jiao Tong University
- Organisation type
- Academia,Academia,Academia,Academia,Academia
- Country
- China, United Kingdom of Great Britain and Northern Ireland, Hong Kong
- Published
- 24 March 2024
- Authors
- Linglin Jing, Sheng Xu, Yifan Wang, Yuzhe Zhou, Tao Shen, Zhigang Ji, Hui Fang, Zhen Li, Siqi Sun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein-ligand contact 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
- 320,400 tokens
Total Proteins = 573 + 495 = 1068 Datapoints = 1068 x 300 = 320,400 Final result = 3.2e5
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
- CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
CrossBind was published by Shanghai AI Lab,Fudan University,Loughborough University,Chinese University of Hong Kong (CUHK),Shanghai Jiao Tong University, in China, in March 2024. academia,Academia,Academia,Academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein-ligand contact prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
It was trained on about 320,400 tokens of text.
Answers
CrossBind — common questions
How many parameters does CrossBind have?
No parameter count has been published for CrossBind, which is why no memory or speed figure appears on this page.
Who created CrossBind?
CrossBind was published by Shanghai AI Lab,Fudan University,Loughborough University,Chinese University of Hong Kong (CUHK),Shanghai Jiao Tong University, based in China, categorised as academia,Academia,Academia,Academia,Academia.
When was CrossBind released?
CrossBind was published in March 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 CrossBind used for?
CrossBind works in Biology, and is recorded as handling protein-ligand contact 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.
What GPU do I need to run CrossBind?
None. CrossBind 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 CrossBind open source?
The licensing for CrossBind was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.