DFMDock
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
- Johns Hopkins University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 28 September 2024
- Authors
- Lee-Shin Chu, Sudeep Sarma, Jeffrey J Gray
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein-ligand binding affinity 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.
- Training data
- tokens
"We trained our model on DIPS-hetero, a subset of DIPS [43 , 44] with approximately 11k heterodimers."
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
- Unreleased
The inference code, model weights, and test set are available at https://github.com/Graylab/DFMDock MIT license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Unified Sampling and Ranking for Protein Docking with DFMDock
- Last updated
- 28 November 2025
What the numbers mean
About this model
DFMDock was published by Johns Hopkins University, in United States of America, in September 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein-ligand binding affinity prediction.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Answers
DFMDock — common questions
When was DFMDock released?
DFMDock was published in September 2024. 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 DFMDock used for?
DFMDock works in Biology, and is recorded as handling protein-ligand binding affinity prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download DFMDock?
The weights for DFMDock are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run DFMDock?
We cannot say. DFMDock 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 DFMDock open source?
Its weights are published, so DFMDock 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 DFMDock have?
No parameter count has been published for DFMDock, which is why no memory or speed figure appears on this page.
Who created DFMDock?
DFMDock was published by Johns Hopkins University, based in United States of America, categorised as 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.