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AI drug discovery models/Genomics, DNA & RNA/LucaOne
model-family passport · Review date not recorded

LucaOne

A unified biological foundation model spanning DNA, RNA and protein sequence.

3/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using LucaOne?

SupportedEvidence supports the stated context with explicit boundaries
Best suited forRepresentation · Prediction
Evidence supportsDNA/RNA/protein downstream tasks: Peer-reviewed
Evidence does not establishUniversal superiority, therapeutic success, clinical utility or regulatory acceptance.
Major limitationPerformance depends on the evaluation dataset and operating conditions.
CompareAdd to evaluationView evidence
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

LucaOne jointly learns from nucleic-acid and protein sequences, creating cross-central-dogma representations.

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
Claims1 normalizedGenomic sequence modelling
EntityLucaOnemodel-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
OrganizationBioMap / collaborators
Model family introducedNot normalized
AccessLimited open access
Commercial useAllowed / verify checkpoint terms
DeploymentHybrid
ComputeGPU recommended
Domainsgenomics · rna · protein
Biology → representation → computation → evidence

How LucaOne represents biology

model-familygenomicsrnaprotein

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

1 · Biological inputs
DNA, RNA or protein sequence
2 · Input representation
Biological sequence tokens
3 · Internal representation
Unified contextual embeddings
4 · Architecture
Transformer encoder
5 · Learning objective
Multi-molecule self-supervised pretraining
6 · Output representation
Dense vectorsScores

Biological scale

genomernaprotein

Modalities & tasks

DNARNAProteinRepresentationPrediction

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 →

Inputs and outputs

Inputs

DNA, RNA or protein sequence

Outputs

Cross-domain embeddingsTask predictions

Scientific and technical profile

Scientific principles

Unified biological language modellingCross-modal transfer

Technology

Transformer encoderMulti-molecule sequence pretraining
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.

Computational intelligence

Transformer self-attention

Ashish Vaswani and colleagues

Protein, genome, molecule and single-cell foundation models use attention to learn dependencies across biological sequences and multimodal inputs.

Matched concepts: transformer, language model, sequence
Computational intelligence

Information, entropy and communication

Claude E. Shannon

Sequence modelling, cross-entropy training, language models, mutual information and representation learning all use Shannon’s framework.

Matched concepts: language model, sequence, representation
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: dna, nucleic, sequence
Genomics & cell systems

DNA as the hereditary transforming principle

Oswald Avery, Colin MacLeod & Maclyn McCarty

Genomics, variant interpretation, gene therapy and sequence foundation models depend on DNA being the durable molecular carrier of biological information.

Matched concepts: dna, sequence

Evaluation evidence

Dataset or evaluationDNA/RNA/protein downstream tasks
Task or metricCross-domain biological representation
Evidence statusPeer-reviewed
Open source ↗

Task-specific evidence only; not comparable as a universal leaderboard score.

Genomic sequence modelling

DNA/RNA/protein downstream tasks

Version history not yet curated · Split details not yet normalized
peer-reviewed

A structured benchmark claim is recorded; consult the linked source for numeric values and protocol details.

Claim caveats
  • Protocol, split and implementation details must match before comparing this claim with another result.
Open benchmark claim →

Known limitations

  • Performance depends on the evaluation dataset and operating conditions.
  • Task-specific benchmark results should not be compared across unlike domains.
  • Outputs require task-specific scientific and experimental validation.

Milestones

Not normalized

Trained across very large species diversity.