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AI drug discovery models/Platforms, Data & Infra/Cradle Bio
platform passport · Review date not recorded

Cradle Bio

Generative protein engineering for any wet lab.

1/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using Cradle Bio?

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 limitationIndependent reproducibility is limited by proprietary access.
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

Cradle offers a software platform that lets protein engineers use generative ML to propose sequence variants with improved properties (stability, activity, expression), feeding lab results back to sharpen predictions. It aims to make ML-guided protein optimization a routine tool rather than a specialist project.

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
EntityCradle Bioplatform · Version history not yet curated
ReviewReview date not recordedReview date not claimed
ConclusionContext requiredAdd to an evaluation before operational use

Model passport

Entity typeplatform
OrganizationCradle
Platform introduced / founded2021
AccessProprietary
Commercial useVendor terms
DeploymentVendor managed
ComputePlatform dependent
Domainsplatform
Biology → representation → computation → evidence

How Cradle Bio 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

ML-guided protein optimizationActive learningDesign–test loops

Technology

Generative sequence modelsProperty predictorsLab-in-the-loop SaaS
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.

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: sequence, variant
Medicinal chemistry & pharmacology

Quantitative structure–activity relationships

Corwin Hansch

Classical QSAR established the central premise that molecular features can predict potency and guide optimization—the conceptual ancestor of modern molecular machine learning.

Matched concepts: activity, optimization
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
Molecular recognition

Enzyme kinetics and saturation

Leonor Michaelis & Maud Menten

Potency, enzyme inhibition, target engagement, metabolic clearance and mechanistic pharmacology routinely use this kinetic framework.

Matched concepts: activity

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

  • Independent reproducibility is limited by proprietary access.
  • A structured benchmark claim has not yet been extracted for this record.
  • Outputs require task-specific scientific and experimental validation.

Milestones

2021

Founder previously led products at Google.

Evidence

Targets everyday R&D teams, not just AI labs.