AI models
GGNN
Training compute
7.6×10²¹ FLOP
Published
Aug 5, 2023
GGNN is an AI model developed by Westlake University, Tsinghua University and Toyota Technological Institute at Chicago (China and United States), first published in August 2023. It works in the biology domain, on tasks such as proteins, protein interaction prediction, protein protein binding affinity prediction and protein representation learning.
Training it took an estimated 7.6×10²¹ FLOP of compute (estimation method: other). Training ran on 2 NVIDIA A100 SXM4 80 GB.
Access: Unreleased. Its weights are not openly released. It is built on top of ESM2-650M. The reference paper has 43 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Westlake University, Tsinghua University, Toyota Technological Institute at Chicago
- Country of organization
- China, United States
- Domain
- Biology
- Task
- Proteins, Protein interaction prediction, Protein protein binding affinity prediction, Protein representation learning
- Training compute
- 7.6×10²¹ FLOP
- Compute estimation method
- Other
- Training hardware
- NVIDIA A100 SXM4 80 GB
- Chips used
- 2
- Training power draw
- 1.6 kW
- Model accessibility
- Unreleased
- Open weights
- No
- Base model
- ESM2-650M
- Citations
- 43
- Epoch confidence
- Confident
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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