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AI drug discovery models/RNA Models & Design/RiNALMo
model-family passport · Review date not recorded

RiNALMo

A large RNA language model for transferable nucleotide representations.

4/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using RiNALMo?

SupportedEvidence supports the stated context with explicit boundaries
Best suited forRepresentation · Prediction
Evidence supportsRNA downstream and structure 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

RiNALMo learns contextual RNA representations with masked-language modelling and transfers to RNA structure and downstream tasks.

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.

Sources3 connectedPrimary resources and normalized claims
Claims1 normalizedIntegrated discovery platform
EntityRiNALMomodel-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
OrganizationUniversity of Zagreb / A*STAR collaborators
Model family introducedNot normalized
AccessOpen source
Commercial useAllowed / verify checkpoint terms
DeploymentSelf-hosted
ComputeGPU recommended
Domainsrna
Biology → representation → computation → evidence

How RiNALMo represents biology

model-familyrna

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

1 · Biological inputs
RNA sequence
2 · Input representation
Nucleotide tokens
3 · Internal representation
Contextual RNA embeddings
4 · Architecture
RNA Transformer
5 · Learning objective
Masked language modelling
6 · Output representation
Dense vectorsStructure labels / scores

Biological scale

rnanucleotide

Modalities & tasks

RNARepresentationPrediction

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

RNA sequence

Outputs

RNA embeddingsStructure/function predictions

Scientific and technical profile

Scientific principles

RNA language modellingTransfer learning

Technology

Transformer encoderSingle-nucleotide tokens
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

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
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

Evaluation evidence

Dataset or evaluationRNA downstream and structure tasks
Task or metricRNA representation / structure prediction
Evidence statusPeer-reviewed
Open source ↗

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

Integrated discovery platform

RNA downstream and structure 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

Evaluated on RNA structure and downstream tasks.