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OpenFold

By AlQuraishi Laboratory / OpenFold contributors →

Trainable PyTorch implementation of the AlphaFold 2 approach

Trainable PyTorch implementation of the AlphaFold 2 approach. Training and inference have different resource needs; code and downloaded parameters have separate terms.

Trainable PyTorch implementation of the AlphaFold 2 approachInput: Protein sequences, alignments and training or inference configurationOutput: Protein structures or trained folding models
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Overview

Trainable PyTorch implementation of the AlphaFold 2 approach. Training and inference have different resource needs; code and downloaded parameters have separate terms.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 16/09/2026, 17:33:40

Key Features

  • Trainable PyTorch implementation of the AlphaFold 2 approach
  • Input: Protein sequences, alignments and training or inference configuration
  • Output: Protein structures or trained folding models
Interactive 3D Structure

OpenFold Predicted Complex

Streams real 3D atomic coordinates from RCSB Protein Data Bank
⇄ Drag to rotate · Scroll to zoom
pLDDT / B-Factor:
>90 Very high 70-90 Confident 50-70 Low <50 Very low

Academic Context & Research Evidence

Biological & Workflow Fit

Compute & Hardware
Consult linked installation guidance; workload dependent
Licensing & Academic Use
Review the linked release, model weights, datasets and service terms separately
Documented Evidence
Project or provider documentation; no local performance benchmark

Cite this Tool

Use this citation format when referencing OpenFold in scientific publications and benchmark papers.

@software{openfold_2026,
  title = {{OpenFold}},
  author = {{AlQuraishi Laboratory / OpenFold contributors}},
  year = {2026},
  url = {https://github.com/aqlaboratory/openfold},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for OpenFold.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeResearch software / model
Access modePublic code / project terms
AI roleScientific machine learning
Input dataProtein sequences, alignments and training or inference configuration
Output dataProtein structures or trained folding models
Licence conditionsReview the linked release, model weights, datasets and service terms separately
Commercial eligibilityNot independently confirmed
Compute requirementsConsult linked installation guidance; workload dependent
ValidationEvaluate on representative data; no clinical suitability established
TypeNot recorded
Intended useBiological and pharmaceutical research
CompatibilityNot recorded
LimitationsTraining and inference have different resource needs; code and downloaded parameters have separate terms.
Evidence levelProject or provider documentation; no local performance benchmark
AvailabilitySee project access and maintenance status
Price / accessSee project terms; compute costs may apply

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

No licensed product photograph recorded for OpenFold yet. Visit the official resource below for manufacturer imagery.

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FAQ

Where is this product available?

See project access and maintenance status

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating OpenFold.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5