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AI drug discovery models/AI-Native Discovery Cos./Valence Labs (MolGPS / LOWE)
platform passport · Review date not recorded

Valence Labs (MolGPS / LOWE)

Recursion's AI research engine for chemistry and biology.

1/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using Valence Labs (MolGPS / LOWE)?

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

Valence Labs, the research arm within Recursion, develops foundation models and methods for molecular ML — including MolGPS (a large graph-based molecular foundation model), phenomics models, and open community efforts (Polaris benchmarks, the LOWE agentic interface). It bridges academic-style ML research and industrial drug discovery.

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.

Sources1 connectedPrimary resources and normalized claims
Claims0 normalizedNo normalized claim yet
EntityValence Labs (MolGPS / LOWE)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 / founded2021
AccessLimited open access
Commercial useAllowed / verify checkpoint terms
DeploymentHybrid
ComputeVendor managed
Domainscompany
Biology → representation → computation → evidence

How Valence Labs (MolGPS / LOWE) represents biology

platformcompany

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

1 · Biological inputs
Disease hypothesis and multimodal evidence
2 · Input representation
Organization / platform dependent
3 · Internal representation
Multiple systems
4 · Architecture
Organization / discovery system
5 · Learning objective
Integrated discovery
6 · Output representation
Programs and evidence

Biological scale

Modalities & tasks

MultimodalPlatformDiscovery

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

Disease hypothesis and multimodal evidence

Outputs

Targets, candidates or development programs

Scientific and technical profile

Scientific principles

Molecular foundation modelsGraph representation learningOpen benchmarking

Technology

MolGPS graph transformerMulti-task molecular pretrainingAgentic tooling (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.

Medicinal chemistry & pharmacology

Selective toxicity and the ‘magic bullet’

Paul Ehrlich

Target selectivity, therapeutic index and mechanism-based screening remain central goals of drug discovery.

Matched concepts: target, drug discovery
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, multimodal, foundation model
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

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

2021

MolGPS scaled molecular graph models to new sizes.

Evidence

Runs the Polaris open benchmarking initiative.