AI models
DiffDock-PP
Training compute
3.5×10¹⁶ FLOP
Parameters
1.6M
Published
Apr 8, 2023
DiffDock-PP is an AI model developed by Technical University of Munich and Massachusetts Institute of Technology (MIT) (Germany and United States), first published in April 2023. It works in the biology domain, on tasks such as protein interaction prediction.
Training it took an estimated 3.5×10¹⁶ FLOP of compute. The model has 1,620,000 parameters. It was trained on roughly 42.8K datapoints.
The reference paper has 67 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- Technical University of Munich, Massachusetts Institute of Technology (MIT)
- Country of organization
- Germany, United States
- Domain
- Biology
- Task
- Protein interaction prediction
- Training compute
- 3.5×10¹⁶ FLOP
- Parameters
- 1,620,000
- Dataset size
- 42.8K
- Citations
- 67
- Epoch confidence
- Likely
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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