GraphMS
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
- Dalian University of Technology,Dongbei University of Technology,Baidu,China National Health Development Research Center
- Organisation type
- Academia,Academia,Industry,Academia
- Country
- China
- Published
- 4 April 2021
- Authors
- Shicheng Cheng, Liang Zhang, Bo Jin, Qiang Zhang, Xinjiang Lu, Mao You, Xueqing Tian
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein-ligand contact 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
- 963,423 tokens
- Epochs
- 10
Edges: Drug-Protein: 1,923 Drug-Disease: 199,214 Protein-Disease: 1,596,745 Drug-Drug: 10,036 Protein-Protein: 7,363 1,923 + 199,214 + 1,596,745 + 10,036 + 7,363 = 1,815,281 total edges Final estimate: 1.8 × 10⁶ "We trained the model for 10 epochs, where each epoch contained 100 steps."
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
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
- 14
Sources
Where this record came from and when it was last checked.
- Reference
- GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures
- Last updated
- 28 November 2025
What the numbers mean
What this model is
GraphMS was published by Dalian University of Technology,Dongbei University of Technology,Baidu,China National Health Development Research Center, in China, in April 2021. The organisation is categorised as academia,Academia,Industry,Academia.
It works in Biology, and is recorded as doing protein-ligand contact prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training set ran to roughly 963,423 tokens.
Answers
GraphMS — common questions
What GPU do I need to run GraphMS?
None. GraphMS 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 GraphMS open source?
No. GraphMS has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GraphMS have?
No parameter count has been published for GraphMS, which is why no memory or speed figure appears on this page.
Who created GraphMS?
GraphMS was published by Dalian University of Technology,Dongbei University of Technology,Baidu,China National Health Development Research Center, based in China, categorised as academia,Academia,Industry,Academia.
When was GraphMS released?
GraphMS was published in April 2021. 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 GraphMS used for?
GraphMS works in Biology, and is recorded as handling protein-ligand contact prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.