What it is
Protenix is a trainable, open-source reproduction of the AlphaFold 3 architecture from ByteDance, released so the community can train and extend an AF3-class model without weight restrictions.
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 Protenix 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
Version history not yet curated
1 version record · release year not yet normalized. 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 or structure contextOutputs
3D 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.
Denoising diffusion generative models
Jascha Sohl-Dickstein, Jonathan Ho and collaboratorsModern protein-backbone, molecular-pose and biomolecular-complex generators use diffusion to sample valid three-dimensional structures and designs.
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.
Levinthal’s paradox and efficient folding pathways
Cyrus LevinthalModern folding algorithms, energy landscapes, learned priors and diffusion models solve a constrained search problem rather than brute-force conformational enumeration.
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.
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
Part of a wave of open AF3 reimplementations (with Boltz, Chai).