KnoMol
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
- Zhejiang University (ZJU),Jiangsu University of Technology,Zhejiang University School of Medicine
- Organisation type
- Academia,Academia
- Country
- China
- Published
- 25 September 2024
- Authors
- Jian Gao, Zheyuan Shen, Yan Lu, Liteng Shen, Binbin Zhou, Donghang Xu, Haibin Dai, Lei Xu, Jinxin Che, Xiaowu Dong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Molecular property 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
- 401,646 tokens
Largest experiment: QM9 with 133,882 examples. Regression task with 1 prediction target.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- KnoMol: A Knowledge-Enhanced Graph Transformer for Molecular Property Prediction
- Last updated
- 28 November 2025
What the numbers mean
What this model is
KnoMol was published by Zhejiang University (ZJU),Jiangsu University of Technology,Zhejiang University School of Medicine, in the country recorded as China, during September 2024. The category the publisher falls under is academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of molecular property prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
It was trained on a corpus of about 401,646 tokens of text.
Answers
KnoMol — common questions
KnoMol— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of molecular property prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
KnoMol— what GPU do I need to run it?
None. This 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.
KnoMol— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
KnoMol— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
KnoMol— who created it?
It was published by Zhejiang University (ZJU),Jiangsu University of Technology,Zhejiang University School of Medicine, based in China, an organisation categorised as academia,Academia.
KnoMol— when was it released?
It 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.
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.