FABind
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
- Renmin University of China,Huazhong University of Science and Technology,Microsoft Research AI for Science,University of Science and Technology of China (USTC)
- Organisation type
- Academia,Academia,Industry,Academia
- Country
- China, United States of America
- Published
- 9 January 2024
- Authors
- Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan
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
- 6,746,610 tokens
- Epochs
- 500
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA V100
- Chips used
- 8
- Power draw
- 4.8 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- FABind: Fast and Accurate Protein-Ligand Binding
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
FABind was published by Renmin University of China,Huazhong University of Science and Technology,Microsoft Research AI for Science,University of Science and Technology of China (USTC), in China, in January 2024. The organisation is categorised as academia,Academia,Industry,Academia.
It works in Biology, and is recorded as doing protein-ligand contact prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Around 6,746,610 tokens went into training it.
Answers
FABind — common questions
Is FABind open source?
The licensing for FABind 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 FABind have?
No parameter count has been published for FABind, which is why no memory or speed figure appears on this page.
Who created FABind?
FABind was published by Renmin University of China,Huazhong University of Science and Technology,Microsoft Research AI for Science,University of Science and Technology of China (USTC), based in China, categorised as academia,Academia,Industry,Academia.
When was FABind released?
FABind was published in January 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 FABind used for?
FABind works in Biology, and is recorded as handling protein-ligand contact prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run FABind?
None. FABind 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.
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.