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model-family passport · Review date not recorded

HelixFold3

An open AF3-class biomolecular complex predictor from the PaddleHelix ecosystem.

2/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using HelixFold3?

UnresolvedEvidence direction is incomplete or not yet resolved
Best suited forPrediction
Evidence supportsPrimary links may be present, but BioAtlas does not claim a review date without a record-level timestamp.
Evidence does not establishUniversal superiority, therapeutic success, clinical utility or regulatory acceptance.
Major limitationPerformance depends on the evaluation dataset and operating conditions.
Current registry recordVersion history not yet curated1 recorded release · Review date not recorded. A newer version is not assumed to be universally better.

What it is

HelixFold3 reproduces AlphaFold3-class complex prediction for proteins, nucleic acids and conventional ligands, with an open academic implementation and web/API access.

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.

Sources2 connectedPrimary resources and normalized claims
Claims0 normalizedNo normalized claim yet
EntityHelixFold3model-family · Version history not yet curated
ReviewReview date not recordedReview date not claimed
ConclusionContext requiredAdd to an evaluation before operational use

Model passport

Entity typemodel-family
OrganizationPaddleHelix / Baidu
Model family introducedNot normalized
AccessLimited open access
Commercial useRestricted / verify terms
DeploymentHybrid
ComputeGPU recommended
Domainsstructure
Biology → representation → computation → evidence

How HelixFold3 represents biology

model-familystructure

Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.

1 · Biological inputs
Biomolecular complex specification
2 · Input representation
Sequence / molecular tokensOptional evolutionary/template context
3 · Internal representation
Pair / geometric representations
4 · Architecture
AF3-class multimodal diffusion model
5 · Learning objective
All-atom structure prediction
6 · Output representation
All-atom coordinatesConfidence scores

Biological scale

proteincomplexmolecule

Modalities & tasks

ProteinDNARNALigandPrediction

Registry, claims and frontier intelligence

Versioned registry

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 →
Benchmark claim ledger

0 normalized claims

No task, dataset, split and metric claim has been normalized for this record yet.

Open claim intelligence →

Inputs and outputs

Inputs

Biomolecular complex specification

Outputs

Biomolecular complex structuresConfidence estimates

Scientific and technical profile

Scientific principles

Multimodal complex predictionDiffusion structural modelling

Technology

AF3-class architectureAll-atom prediction
Ideas before algorithms

Scientific lineage

Explore all foundations

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.

Genomics & cell systems

X-ray evidence for the helical structure of DNA

Rosalind Franklin & Raymond Gosling

Structural genomics and sequence-to-structure reasoning began with experimentally grounded molecular geometry.

Matched concepts: helix, nucleic, structure
Structural biology

First atomic structures of globular proteins

John Kendrew & Max Perutz

Protein structure prediction and structure-based design became meaningful because experimental crystallography established the target reality to predict against.

Matched concepts: fold, complex
Structural biology

Anfinsen’s dogma—the thermodynamic hypothesis

Christian B. Anfinsen

Protein structure prediction, inverse folding and generative protein design all assume that sequence strongly constrains structure and function.

Matched concepts: fold, protein
Structural biology

Levinthal’s paradox and efficient folding pathways

Cyrus Levinthal

Modern folding algorithms, energy landscapes, learned priors and diffusion models solve a constrained search problem rather than brute-force conformational enumeration.

Matched concepts: fold, diffusion

Evaluation evidence

Dataset or evaluationNot yet curated
Task or metricNot yet extracted
Evidence statusPrimary paper linked; benchmark extraction pending
Open source ↗

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

Not normalized

Technical report compares the model with AlphaFold3-class tasks.