DeepConPred2
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
- Organisation type
- Academia
- Country
- China
- Published
- 11 October 2018
- Authors
- Wenze Ding, Wenzhi Mao, Di Shao, Wenxuan Zhang, Haipeng Gong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein folding prediction, Protein contact and distance 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
- 38,733,750 tokens
3,443 proteins × (150 × 149)/2 pairs/protein = 3.84 × 10⁷ datapoints ≈ 3.8 × 10⁷
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
- 31
Sources
Where this record came from and when it was last checked.
- Reference
- DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
- Last updated
- 28 November 2025
What the numbers mean
About this model
DeepConPred2 was published by Tsinghua University, in China, in October 2018. It comes out of academia.
It works in Biology, and is recorded as doing proteins, Protein folding prediction, Protein contact and distance 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
The training set ran to roughly 38,733,750 tokens.
Answers
DeepConPred2 — common questions
What GPU do I need to run DeepConPred2?
None. DeepConPred2 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 DeepConPred2 open source?
The licensing for DeepConPred2 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 DeepConPred2 have?
No parameter count has been published for DeepConPred2, which is why no memory or speed figure appears on this page.
Who created DeepConPred2?
DeepConPred2 was published by Tsinghua University, based in China, categorised as academia.
When was DeepConPred2 released?
DeepConPred2 was published in October 2018. 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 DeepConPred2 used for?
DeepConPred2 works in Biology, and is recorded as handling proteins, Protein folding prediction, Protein contact and distance prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.