What it is
OpenFold is a from-scratch, trainable PyTorch reimplementation of AlphaFold 2 with open weights and training data (OpenProteinSet). It let the whole community fine-tune and study folding models rather than only run inference, and seeded a nonprofit consortium for open biomolecular AI.
Evidence trail
BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.
Model passport
How OpenFold represents biology
Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.
Biological scale
Modalities & tasks
Registry, claims and frontier intelligence
OpenFold3 Preview
2 version records · latest curated year 2026. Model-family identity remains separate from capability and access changes.
Explore version lineage →0 normalized claims
No task, dataset, split and metric claim has been normalized for this record yet.
Open claim intelligence →1 connected frontier
Open DDEs · Recent preprint
Inspect research horizon →Connected research frontiers
These records describe active research directions, not guaranteed capabilities of this model. Evidence stages and unresolved questions are preserved separately.
Open reproductions of frontier drug-design engines
Aureka AI OpenDDE project · 2026-07-04Can the community reproduce and extend proprietary all-atom drug-design engines with open training code, checkpoints and benchmarks?
Evidence boundary and unresolved questions
OpenDDE is a very recent July 2026 preprint. Its claimed parity has not yet received broad independent evaluation.
- Can external teams reproduce the reported training and benchmark results?
- What data provenance and leakage controls are documented?
- How do open checkpoints perform in prospective discovery projects?
open science · co-folding · reproducibility · scaling lawsOpen frontier record →Inputs and outputs
Inputs
Biomolecular sequence / complex specificationOptional MSA / templates depending on releaseOutputs
3D biomolecular structuresConfidence estimatesScientific and technical profile
Scientific principles
Technology
Scientific lineage
These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.
Anfinsen’s dogma—the thermodynamic hypothesis
Christian B. AnfinsenProtein structure prediction, inverse folding and generative protein design all assume that sequence strongly constrains structure and function.
Protein sequence databases, evolutionary substitution matrices and computational comparison
Margaret Oakley DayhoffProtein language models, homology inference, multiple-sequence alignments and evolutionary priors inherit her conversion of sequence biology into computable data.
Atomic structures of biologically important molecules by X-ray crystallography
Dorothy Crowfoot HodgkinStructure-based drug design depends on the experimental structural tradition she helped establish.
The alpha helix, beta sheet and hydrogen-bonded protein structure
Linus Pauling, Robert Corey & Herman BransonProtein representation, fold recognition, structural priors and generative protein design all encode these recurring geometric motifs.
X-ray evidence for the helical structure of DNA
Rosalind Franklin & Raymond GoslingStructural genomics and sequence-to-structure reasoning began with experimentally grounded molecular geometry.
First atomic structures of globular proteins
John Kendrew & Max PerutzProtein structure prediction and structure-based design became meaningful because experimental crystallography established the target reality to predict against.
Evaluation evidence
BioAtlas has not yet extracted a structured benchmark claim for this record.
Known limitations
- Performance depends on the evaluation dataset and operating conditions.
- A structured benchmark claim has not yet been extracted for this record.
- Outputs require task-specific scientific and experimental validation.
Milestones
Revealed how folding models learn during training.
Runs as a nonprofit consortium.