PocketGen
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 Science and Technology of China (USTC),Hefei Comprehensive National Science Center,Harvard University,Broad Institute,Harvard Data Science Initiative
- Organisation type
- Academia,Research collective,Academia,Research collective,Academia
- Country
- China, United States of America
- Published
- 23 September 2024
- Authors
- Zaixi Zhang, Wan Xiang Shen, Qi Liu, Marinka Zitnik
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.
- Parameters
- 7.9M
- Training data
- tokens
"As a result, PocketGen requires significantly fewer trainable parameters than RFDiffusionAA [16] (7.9M versus 82.9M trainable parameters)."
"Finally, we have 40k protein-ligand pairs for training, 100 pairs for validation, and 100 pairs for testing." 40000*300 (assumed length) = 12000000
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
- 2.1 × 10¹⁹ FLOP
- How it was established
- Hardware
"It takes around 48 hours to finish training on 1 Tesla A100 GPU from scratch." Assume 40% utilization, FP16 tensor precision.
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
- 48 hours
- Power draw
- 433 W
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 15
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Generation of Protein Pockets with PocketGen
- Last updated
- 28 November 2025
What the numbers mean
What this model is
PocketGen was published by University of Science and Technology of China (USTC),Hefei Comprehensive National Science Center,Harvard University,Broad Institute,Harvard Data Science Initiative, in the country recorded as China, during September 2024. The publishing organisation is categorised as academia,Research collective,Academia,Research collective,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.
How it was trained
Producing it required arithmetic totalling around 2.1 × 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
PocketGen — common questions
PocketGen— 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.
PocketGen— how many parameters does it have?
It has a parameter count of 7.9M. "As a result, PocketGen requires significantly fewer trainable parameters than RFDiffusionAA [16] (7.9M versus 82.9M trainable parameters).". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
PocketGen— who created it?
It was published by University of Science and Technology of China (USTC),Hefei Comprehensive National Science Center,Harvard University,Broad Institute,Harvard Data Science Initiative, based in China, an organisation categorised as academia,Research collective,Academia,Research collective,Academia.
PocketGen— when was it released?
It 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.
PocketGen— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein-ligand contact prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
PocketGen— how much compute was used to train it?
Training consumed around 2.1 × 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.
PocketGen— 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.
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.