BindDM
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
- Peng Cheng Laboratory,Peking University,University of Science and Technology of China (USTC),ByteDance,Tsinghua University
- Organisation type
- Academia,Academia,Academia,Industry,Academia
- Country
- China
- Published
- 24 March 2024
- Authors
- Zhilin Huang, Ling Yang, Zaixi Zhang, Xiangxin Zhou, Yu Bao, Xiawu Zheng, Yuwei Yang, Yu Wang, Wenming Yang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery, Protein-ligand contact prediction, 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
- 10,000,000 tokens
100,000 protein-ligand pairs Total datapoints = 100,000 Final result = 1.0e5
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
- Open (non-commercial)
no clear license https://github.com/YangLing0818/BindDM
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
- 14
Sources
Where this record came from and when it was last checked.
- Reference
- Binding-Adaptive Diffusion Models for Structure-Based Drug Design
- Last updated
- 1 December 2025
What the numbers mean
About this model
BindDM was published by Peng Cheng Laboratory,Peking University,University of Science and Technology of China (USTC),ByteDance,Tsinghua University, in China, in March 2024. It comes out of academia,Academia,Academia,Industry,Academia.
It works in Biology, and is recorded as doing drug discovery, Protein-ligand contact prediction, Protein-ligand binding affinity prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training set ran to roughly 10,000,000 tokens.
Answers
BindDM — common questions
What is BindDM used for?
BindDM works in Biology, and is recorded as handling drug discovery, Protein-ligand contact prediction, Protein-ligand binding affinity prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run BindDM?
None. BindDM 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 BindDM open source?
No. BindDM has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does BindDM have?
No parameter count has been published for BindDM, which is why no memory or speed figure appears on this page.
Who created BindDM?
BindDM was published by Peng Cheng Laboratory,Peking University,University of Science and Technology of China (USTC),ByteDance,Tsinghua University, based in China, categorised as academia,Academia,Academia,Industry,Academia.
When was BindDM released?
BindDM was published in March 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.
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.