GGNN
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
- Westlake University,Tsinghua University,Toyota Technological Institute at Chicago
- Organisation type
- Academia,Academia,Academia
- Country
- China, United States of America
- Published
- 5 August 2023
- Authors
- Fang Wu, Lirong Wu, Dragomir Radev, Jinbo Xu and Stan Z. Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein interaction prediction, Protein protein binding affinity prediction, Protein representation learning
- Base model
- ESM2-650M
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
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
- 7.6 × 10²¹ FLOP
- How it was established
- Other
ESM-2 650M is very likely the majority of FLOPs, since they only used 2 A100s (ESM-2 650M used 512 V100s for 8 days). As such I'm reporting the compute from ESM-2 650M here only.
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 SXM4 80 GB
- Chips used
- 2
- Power draw
- 1.6 kW
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
- Open source
MIT license https://github.com/smiles724/GNN-Bottleneck
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 43
"In this work, we integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks and evaluate a variety of protein representation learning benchmarks, including protein-protein interface prediction, model quality assessment, protein-protein rigid-body docking, and binding affinity prediction. Our findings show an overall improvement of 20% over baselines."
Sources
Where this record came from and when it was last checked.
- Reference
- Integration of pre-trained protein language models into geometric deep learning networks
- Last updated
- 1 January 2026
What the numbers mean
Background
GGNN was published by Westlake University,Tsinghua University,Toyota Technological Institute at Chicago, in the country recorded as China, during August 2023. It comes out of an organisation categorised as academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of proteins, Protein interaction prediction, Protein protein binding affinity prediction, Protein representation learning.
Rather than being trained from scratch, it is derived from ESM2-650M. That is why it shares the base model's general shape and size.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took a computation budget of roughly 7.6 × 10²¹ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
GGNN — common questions
GGNN— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of proteins, Protein interaction prediction, Protein protein binding affinity prediction, Protein representation learning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
GGNN— how much compute was used to train it?
Training consumed around 7.6 × 10²¹ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 GB. 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.
GGNN— 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.
GGNN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GGNN— 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.
GGNN— who created it?
It was published by Westlake University,Tsinghua University,Toyota Technological Institute at Chicago, based in China, an organisation categorised as academia,Academia,Academia.
GGNN— when was it released?
It was published in August 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.