ParetoDrug
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
- Chinese University of Hong Kong (CUHK),Zhejiang Lab,Zhejiang University (ZJU),Huawei Noah's Ark Lab
- Organisation type
- Academia,Academia,Industry
- Country
- Hong Kong, China
- Published
- 2 September 2024
- Authors
- Yaodong Yang, Guangyong Chen, Jinpeng Li, Junyou Li, Odin Zhang, Xujun Zhang, Lanqing Li, Jianye Hao, Ercheng Wang, Pheng-Ann Heng
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
- tokens
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
The source code of this study is publicly available from the GitHub repository: https://github.com/CNDOTA/ParetoDrug. We also provide the Google Colab version of ParetoDrug, which could be directly run online. (MIT license) pretrained weights of Lmser Transformer
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 1
Sources
Where this record came from and when it was last checked.
- Reference
- Enabling target-aware molecule generation to follow multi objectives with Pareto MCTS
- Last updated
- 28 November 2025
What the numbers mean
About this model
ParetoDrug was published by Chinese University of Hong Kong (CUHK),Zhejiang Lab,Zhejiang University (ZJU),Huawei Noah's Ark Lab, in Hong Kong, in September 2024. The organisation is categorised as academia,Academia,Industry.
It works in Biology, and is recorded as doing drug discovery.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
ParetoDrug — common questions
Is ParetoDrug open source?
Its weights are published, so ParetoDrug 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 ParetoDrug have?
No parameter count has been published for ParetoDrug, which is why no memory or speed figure appears on this page.
Who created ParetoDrug?
ParetoDrug was published by Chinese University of Hong Kong (CUHK),Zhejiang Lab,Zhejiang University (ZJU),Huawei Noah's Ark Lab, based in Hong Kong, categorised as academia,Academia,Industry.
When was ParetoDrug released?
ParetoDrug 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 ParetoDrug used for?
ParetoDrug works in Biology, and is recorded as handling drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download ParetoDrug?
The weights for ParetoDrug 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 ParetoDrug?
We cannot say. ParetoDrug 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.
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.