DiffSBDD (CrossDocked)
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Ecole Polytechnique F´ed´erale de Lausanne (EPFL),University of Cambridge,Cornell University,Chinese Academy of Mathematics and System Science,University of Rome,Microsoft Research,University of Oxford,AITHYRA Institute
- Organisation type
- Academia,Academia,Academia,Academia,Academia,Industry,Academia,Research collective
- Country
- Switzerland, United Kingdom of Great Britain and Northern Ireland, United States of America, China, Italy, Austria
- Published
- 24 October 2022
- Authors
- Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
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
- 2,900,000 tokens
- Epochs
- 1,000
"We use the CrossDocked dataset [26] with 100,000 high-quality protein-ligand pairs for training and 100 proteins for testing"
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
- 2.7 × 10²⁰ FLOP
- How it was established
- Hardware
" For CrossDocked, 100 training epochs take approximately 6 h/8 h in the Cα case and 48 h/60 h per 100 epochs on a single NVIDIA A100 GPU with all atom pocket representation" 312000000000000*0.4*60 hours*10=2.695680e+20
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
- 600 hours (25 days)
- Power draw
- 440 W
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
Code Availability. Our source codes are publicly available at https://github.com/arneschneuing/ (MIT license) DiffSBDD. Model weights can be downloaded from Zenodo: https://zenodo.org/records/8183747 (Creative Commons Attribution 4.0 International)
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
- 404
Sources
Where this record came from and when it was last checked.
- Reference
- Structure-based Drug Design with Equivariant Diffusion Models
- Last updated
- 25 May 2026
What the numbers mean
Background
DiffSBDD (CrossDocked) was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL),University of Cambridge,Cornell University,Chinese Academy of Mathematics and System Science,University of Rome,Microsoft Research,University of Oxford,AITHYRA Institute, in Switzerland, in October 2022. academia,Academia,Academia,Academia,Academia,Industry,Academia,Research collective is the category the publisher falls under.
It works in Biology, and is recorded as doing drug discovery.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
How it was trained
The training run consumed about 2.7 × 10²⁰ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 2,900,000 tokens of text.
Answers
DiffSBDD (CrossDocked) — common questions
Who created DiffSBDD (CrossDocked)?
DiffSBDD (CrossDocked) was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL),University of Cambridge,Cornell University,Chinese Academy of Mathematics and System Science,University of Rome,Microsoft Research,University of Oxford,AITHYRA Institute, based in Switzerland, categorised as academia,Academia,Academia,Academia,Academia,Industry,Academia,Research collective.
When was DiffSBDD (CrossDocked) released?
DiffSBDD (CrossDocked) was published in October 2022. 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 DiffSBDD (CrossDocked) used for?
DiffSBDD (CrossDocked) works in Biology, and is recorded as handling drug discovery. 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.
Where can I download DiffSBDD (CrossDocked)?
The weights for DiffSBDD (CrossDocked) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train DiffSBDD (CrossDocked)?
Around 2.7 × 10²⁰ FLOP, on 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.
What GPU do I need to run DiffSBDD (CrossDocked)?
We cannot say. DiffSBDD (CrossDocked) has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is DiffSBDD (CrossDocked) open source?
Its weights are published, so DiffSBDD (CrossDocked) can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does DiffSBDD (CrossDocked) have?
No parameter count has been published for DiffSBDD (CrossDocked), which is why no memory or speed figure appears on this page.
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.