DiffDock-PP
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
- Technical University of Munich,Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia,Academia
- Country
- Germany, United States of America
- Published
- 8 April 2023
- Authors
- Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stärk, Menghua Wu, Gabriele Corso, Céline Marquet, Regina Barzilay, Tommi S. Jaakkola
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein interaction 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
- 1.6M
- Training data
- 42,826 tokens
- Epochs
- 170
42,826 pairs approximated at 500 tokens 42826*500=21413000
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
- 3.5 × 10¹⁶ FLOP
Using 6ND formula with 170 epochs 6*21413000*1620000*170=3.5382841e+16
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
- 67
Sources
Where this record came from and when it was last checked.
- Reference
- DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
- Last updated
- 25 May 2026
What the numbers mean
About this model
DiffDock-PP was published by Technical University of Munich,Massachusetts Institute of Technology (MIT), in Germany, in April 2023. academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein interaction 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 around 3.5 × 10¹⁶ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
The training set ran to roughly 42,826 tokens.
Answers
DiffDock-PP — common questions
When was DiffDock-PP released?
DiffDock-PP was published in April 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.
What is DiffDock-PP used for?
DiffDock-PP works in Biology, and is recorded as handling protein interaction 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.
How much compute was used to train DiffDock-PP?
Around 3.5 × 10¹⁶ FLOP. 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.
What GPU do I need to run DiffDock-PP?
None. DiffDock-PP 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.
Is DiffDock-PP open source?
The licensing for DiffDock-PP was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does DiffDock-PP have?
DiffDock-PP has 1.6M 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.
Who created DiffDock-PP?
DiffDock-PP was published by Technical University of Munich,Massachusetts Institute of Technology (MIT), based in Germany, categorised as academia,Academia.
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.