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AI drug discovery models/Virtual Cells & Single-Cell/scFoundation
model-family passport · Review date not recorded

scFoundation

A 100M-parameter model of ~20,000 genes at once.

2/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using scFoundation?

UnresolvedEvidence direction is incomplete or not yet resolved
Best suited forSimulation · Prediction
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

scFoundation is a large-scale single-cell model trained on ~50M cells that reads the full ~20,000-gene expression vector, supporting read-depth enhancement, perturbation and drug-response prediction. It is associated with BioMap's broader life-science foundation-model effort.

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
EntityscFoundationmodel-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
OrganizationTsinghua / BioMap
Model family introducedNot normalized
AccessOpen source
Commercial useAllowed / verify checkpoint terms
DeploymentSelf-hosted
ComputeGPU recommended
Domainscells
Biology → representation → computation → evidence

How scFoundation represents biology

model-familycells

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

1 · Biological inputs
Single-cell or perturbation data
2 · Input representation
Gene-expression representation
3 · Internal representation
Cell-state embedding
4 · Architecture
Single-cell foundation model
5 · Learning objective
Cell representation / transition learning
6 · Output representation
Cell embeddingGene-expression state

Biological scale

Modalities & tasks

CellTranscriptomicsSimulationPredictionRepresentation

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

Single-cell or perturbation data

Outputs

Cell statesPerturbation predictions

Scientific and technical profile

Scientific principles

Single-cell foundation modelingFull-transcriptome context

Technology

Asymmetric encoder–decoderRead-depth-aware pretraining
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

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: expression, transcript, perturbation
Genomics & cell systems

The epigenetic landscape and cell-fate trajectories

Conrad H. Waddington

Single-cell embeddings, trajectory inference, reprogramming and virtual-cell models often represent cell identity as movement through a learned state landscape.

Matched concepts: cell state, single-cell
Molecular recognition

Cooperative ligand binding

Archibald V. Hill

Dose–response curves, receptor occupancy, multisite binding and systems pharmacology still use Hill-type models.

Matched concepts: response
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: state
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

Evaluation evidence

Dataset or evaluationNot yet curated
Task or metricNot yet extracted
Evidence statusPrimary paper linked; benchmark extraction pending
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

Not normalized

Trained on ~50M single cells.

Evidence

Reads all ~20k genes jointly.