HelixFold3
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
- Tecorigin LTD,Tsinghua University,Baidu
- Organisation type
- Industry,Academia,Industry
- Country
- China
- Published
- 30 August 2024
- Authors
- Lihang Liu, Shanzhuo Zhang, Yang Xue, Xianbin Ye, Kunrui Zhu, Yuxin Li, Yang Liu, Wenlai Zhao, Hongkun Yu, Zhihua Wu, Xiaonan Zhang, Xiaomin Fang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction, Protein interaction prediction, Protein nucleotide interaction prediction, RNA-Protein interaction prediction, Protein design, 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
- Closed — provider access only
- Model access
- API access
- Training code
- Open (non-commercial)
https://paddlehelix.baidu.com/ https://paddlehelix.baidu.com/app/tut/guide/all/helixfold3sdk CC BY-NC-SA 4.0 https://github.com/PaddlePaddle/PaddleHelix
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Technical Report of HelixFold3 for Biomolecular Structure Prediction
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
HelixFold3 was published by Tecorigin LTD,Tsinghua University,Baidu, in the country recorded as China, during August 2024. The publishing organisation is categorised as industry,Academia,Industry.
It works in the domain of Biology, and is recorded as performing the task of protein folding prediction, Protein interaction prediction, Protein nucleotide interaction prediction, RNA-Protein interaction prediction, Protein design, Drug discovery.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
HelixFold3 — common questions
HelixFold3— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein folding prediction, Protein interaction prediction, Protein nucleotide interaction prediction, RNA-Protein interaction prediction, Protein design, Drug discovery. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
HelixFold3— 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.
HelixFold3— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
HelixFold3— 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.
HelixFold3— who created it?
It was published by Tecorigin LTD,Tsinghua University,Baidu, based in China, an organisation categorised as industry,Academia,Industry.
HelixFold3— when was it released?
It was published in August 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.