DrugCLIP
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
- Tsinghua University,Tsinghua-Peiking Center for Life Sciences,Peking University,Beijing Academy of Artificial Intelligence / BAAI
- Organisation type
- Academia,Research collective,Academia,Academia
- Country
- China
- Published
- 3 September 2024
- Authors
- Yinjun Jia, Bowen Gao, Jiaxin Tan, Xin Hong, Wenyu Zhu, Haichuan Tan, Yuan Xiao, Yanwen Huang, Yue Jin, Yafei Yuan, Jiekang Tian, Weiying Ma, Yaqin Zhang, Chuangye Yan, Wei Zhang, Yanyan Lan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Molecular screening, Drug discovery
- Base model
- Uni-Mol Molecular Model
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
Data for DrugCLIP: Pretraining: 5.5 × 10^6 (pseudo-pocket and ligand pairs) Fine-tuning: 4.0 × 10^4 (experimental complexes) Total 5,540,000 datapoints Assuming 300 tokens per protein-ligand pair: 5540000*300=1662000000
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.
- How it was established
- Hardware
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
- 3
Sources
Where this record came from and when it was last checked.
- Reference
- Deep contrastive learning enables genome-wide virtual screening
- Last updated
- 1 January 2026
What the numbers mean
What this model is
DrugCLIP was published by Tsinghua University,Tsinghua-Peiking Center for Life Sciences,Peking University,Beijing Academy of Artificial Intelligence / BAAI, in China, in September 2024. It comes out of academia,Research collective,Academia,Academia.
It works in Biology, and is recorded as doing molecular screening, Drug discovery.
It builds on Uni-Mol Molecular Model, which is why it shares that model's general shape and size.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
DrugCLIP — common questions
When was DrugCLIP released?
DrugCLIP 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 DrugCLIP used for?
DrugCLIP works in Biology, and is recorded as handling molecular screening, Drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run DrugCLIP?
None. DrugCLIP 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 DrugCLIP open source?
The licensing for DrugCLIP 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 DrugCLIP have?
No parameter count has been published for DrugCLIP, which is why no memory or speed figure appears on this page.
Who created DrugCLIP?
DrugCLIP was published by Tsinghua University,Tsinghua-Peiking Center for Life Sciences,Peking University,Beijing Academy of Artificial Intelligence / BAAI, based in China, categorised as academia,Research collective,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.