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platform passport · Review date not recorded

Recursion (LOWE + Phenomics)

An industrial-scale phenomics + ML drug-discovery engine.

1/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using Recursion (LOWE + Phenomics)?

UnresolvedEvidence direction is incomplete or not yet resolved
Best suited forPlatform · Training
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

Recursion runs one of the largest wet-lab data factories in biology, generating petabytes of cellular imaging ('phenomaps') to train models linking genes, compounds and disease. Its LOWE agentic interface orchestrates discovery workflows; it completed its acquisition of Exscientia on 20 November 2024 and released open phenomics datasets (RxRx) and molecular-ML research through Valence Labs.

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
EntityRecursion (LOWE + Phenomics)platform · Version history not yet curated
ReviewReview date not recordedReview date not claimed
ConclusionContext requiredAdd to an evaluation before operational use

Model passport

Entity typeplatform
OrganizationRecursion Pharmaceuticals
Platform introduced / founded2013
AccessLimited open access
Commercial useAllowed / verify checkpoint terms
DeploymentHybrid
ComputePlatform dependent
Domainsplatform
Biology → representation → computation → evidence

How Recursion (LOWE + Phenomics) represents biology

platformplatform

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

1 · Biological inputs
Project-specific biological data
2 · Input representation
Model-dependent
3 · Internal representation
Multiple model families
4 · Architecture
Platform / infrastructure
5 · Learning objective
Training, orchestration or inference
6 · Output representation
Model-dependent

Biological scale

Modalities & tasks

MultimodalPlatformTrainingPrediction

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

Project-specific biological data

Outputs

Models, evidence or candidates

Scientific and technical profile

Scientific principles

Phenomics / high-content imagingRepresentation learningCausal maps of biology

Technology

Automated imaging at scaleSelf-supervised vision modelsAgentic workflow (LOWE)
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: representation
Medicinal chemistry & pharmacology

Rational antimetabolite drug design

Gertrude B. Elion & George H. Hitchings

Mechanism-based design, pathway selectivity and iterative medicinal chemistry are direct descendants of this strategy.

Matched concepts: candidate
Biologics & genome engineering

Hybridoma production of monoclonal antibodies

Georges J. F. Köhler & César Milstein

Therapeutic antibodies, diagnostic antibodies and antibody discovery platforms became scalable and reproducible.

Matched concepts: biologic
Computational intelligence

Energy-based associative neural networks

John J. Hopfield

Energy-based learning, associative retrieval and modern attention mechanisms share conceptual roots with this statistical-physics view of computation.

Matched concepts: representation

Evaluation evidence

Dataset or evaluationNot yet curated
Task or metricNot yet extracted
Evidence statusNo task-specific benchmark record curated
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

2013

Completed the acquisition of Exscientia on 20 November 2024.

Evidence

Released the open RxRx phenomics datasets.