Re-Dock
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
- Zhejiang University (ZJU),Westlake University,University of Washington
- Organisation type
- Academia,Academia,Academia
- Country
- China, United States of America
- Published
- 21 February 2024
- Authors
- Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li, Stan. Z. Li
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
- tokens
23496 examples in PDBbind 2020
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 7.5 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: 1x NVIDIA A100 GPU (3.12e14 FLOP/s) 2. Training duration: 7 days (directly provided) = 604,800 seconds 3. Utilization rate: 40% 4. Final calculation: 1 GPU × 3.12e14 FLOP/s × 604,800s × 0.4 = 7.55e19 FLOPs
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 A100
- Chips used
- 1
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 435 W
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 24
Sources
Where this record came from and when it was last checked.
- Reference
- Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Re-Dock was published by Zhejiang University (ZJU),Westlake University,University of Washington, in the country recorded as China, during February 2024. The category the publisher falls under is academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein-ligand contact prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required arithmetic totalling around 7.5 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Re-Dock — common questions
Re-Dock— who created it?
It was published by Zhejiang University (ZJU),Westlake University,University of Washington, based in China, an organisation categorised as academia,Academia,Academia.
Re-Dock— when was it released?
It was published in February 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.
Re-Dock— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of 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.
Re-Dock— how much compute was used to train it?
Training consumed around 7.5 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Re-Dock— 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.
Re-Dock— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Re-Dock— 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.