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model-family passport · Review date not recorded

AIDO Cell 1.0

A multiscale virtual-cell world model for simulating biological interventions and readouts.

2/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using AIDO Cell 1.0?

SupportedEvidence supports the stated context with explicit boundaries
Best suited forSimulation · Prediction
Evidence supportsVirtual Cell Benchmark 1.0: Developer-reported; independent reproduction pending
Evidence does not establishUniversal superiority, therapeutic success, clinical utility or regulatory acceptance.
Major limitationBenchmark leadership claims are developer-reported and should not be treated as independently established.
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

AIDO Cell 1.0 is GenBio AI's general-purpose virtual-cell simulator. It integrates multiple biological scales and modalities into a shared world-model framework and is presented for intervention-conditioned simulation across cell biology tasks. BioAtlas treats performance claims from Virtual Cell Benchmark 1.0 as developer-reported pending independent reproduction.

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 normalizedCell perturbation prediction
EntityAIDO Cell 1.0model-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
OrganizationGenBio AI
Model family introduced2026
AccessLimited open access
Commercial useAllowed / verify checkpoint terms
DeploymentHybrid
ComputeProvider-managed / accelerator recommended
Domainscells · genomics · rna · protein · structure
Biology → representation → computation → evidence

How AIDO Cell 1.0 represents biology

model-familycellsgenomicsrnaproteinstructure

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

1 · Biological inputs
Biological stateGenetic or small-molecule interventionExperimental context
2 · Input representation
Multimodal biological stateIntervention tokens / conditions
3 · Internal representation
Shared multiscale world-model state
4 · Architecture
Multimodal biological world model
5 · Learning objective
Intervention-conditioned simulationCross-scale biological prediction
6 · Output representation
Task-specific biological readoutsCell-state predictionsScores / structures

Biological scale

genomernaproteincell

Modalities & tasks

CellTranscriptomicsGenomicsRNAProteinBiomolecular structureSimulationPredictionPerturbation responseMultiscale modelling

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

Biological stateGenetic or small-molecule interventionExperimental context

Outputs

Simulated biological statesExperimental readout predictionsStructure/regulation/perturbation predictions

Scientific and technical profile

Scientific principles

Virtual-cell simulationMultiscale world modellingClosed-loop model adaptation

Technology

AIDO CellAIDO FoundryMultimodal biological simulation
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.

Molecular recognition

Concerted allostery

Jacques Monod, Jeffries Wyman & Jean-Pierre Changeux

Allosteric drug design exploits remote pockets to modulate function, selectivity and resistance without competing at the active site.

Matched concepts: regulation, state
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: genetic
Genomics & cell systems

Gene regulation and the operon model

François Jacob & Jacques Monod

Target biology, perturbation models, transcriptomic response prediction and virtual cells all require an explicit model of regulated gene programs.

Matched concepts: perturbation
Biologics & genome engineering

Programmable CRISPR–Cas genome editing

Jennifer A. Doudna & Emmanuelle Charpentier

CRISPR enables target validation, disease models, perturbation atlases, functional genomics and gene-editing therapeutics.

Matched concepts: perturbation
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: multimodal

Evaluation evidence

Dataset or evaluationVirtual Cell Benchmark 1.0
Task or metric31 metrics across five task families
Evidence statusDeveloper-reported; independent reproduction pending
Open source ↗

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

Cell perturbation prediction

Virtual Cell Benchmark 1.0

Version history not yet curated · Split details not yet normalized
developer-reported

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.

Known limitations

  • Benchmark leadership claims are developer-reported and should not be treated as independently established.
  • The released system currently demonstrates prototype virtual cells and does not constitute a solved general model of cell biology.
  • Wet-lab validation of novel predictions is still emerging and remains essential for scientific use.

Milestones

2026

Developer-reported Virtual Cell Benchmark 1.0 spans 31 metrics across five task families; independent reproduction remains pending.