RaptorX-Contact
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
- Toyota Technological Institute at Chicago
- Organisation type
- Academia
- Country
- United States of America
- Published
- 2 May 2019
- Authors
- Jinbo Xu, Sheng Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction, Proteins, 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
- tokens
Calculation steps: 1. Training proteins = 11,410 - 900 = 10,510 proteins 2. Residue pairs per protein = (300 × 299)/2 = 44,850 pairs 3. Total data points = 10,510 × 44,850 = 4.73 × 10⁸ Final estimate ≈ 4.5 × 10⁸ data points
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
- Unreleased
- Training code
- Open (restricted use)
this license for code: https://github.com/j3xugit/RaptorX-Contact?tab=GPL-3.0-1-ov-file
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
"On the 32 CASP13 FM (free-modeling) targets with a median MSA (multiple sequence alignment) depth of 36, RaptorX yielded the best contact prediction among 46 groups and almost the best 3D structure modeling among all server groups without time-consuming conformation sampling."
Sources
Where this record came from and when it was last checked.
- Reference
- Analysis of distance-based protein structure prediction by deep learning in CASP13
- Last updated
- 28 November 2025
What the numbers mean
About this model
RaptorX-Contact was published by Toyota Technological Institute at Chicago, in United States of America, in May 2019. It comes out of academia.
It works in Biology, and is recorded as doing protein folding prediction, Proteins, 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.
How it was trained
Its inclusion criterion is sOTA improvement.
Answers
RaptorX-Contact — common questions
How many parameters does RaptorX-Contact have?
No parameter count has been published for RaptorX-Contact, which is why no memory or speed figure appears on this page.
Who created RaptorX-Contact?
RaptorX-Contact was published by Toyota Technological Institute at Chicago, based in United States of America, categorised as academia.
When was RaptorX-Contact released?
RaptorX-Contact was published in May 2019. 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 RaptorX-Contact used for?
RaptorX-Contact works in Biology, and is recorded as handling protein folding prediction, Proteins, Protein contact and distance prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run RaptorX-Contact?
None. RaptorX-Contact 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 RaptorX-Contact open source?
No. RaptorX-Contact has not had its weights published, so it exists only as a service controlled by its owner.
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.