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model-family passport · Review date not recorded

Nabla Bio — JAM

Generative design of antibodies against hard membrane targets.

1/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using Nabla Bio — JAM?

UnresolvedEvidence direction is incomplete or not yet resolved
Best suited forGeneration · Optimization
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.
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

Nabla Bio's JAM ('Joint Atomic Modeling') system designs epitope-specific antibodies — including against notoriously difficult multi-pass membrane proteins like GPCRs — with high specificity and developability, largely in silico. It spun out of the Wyss Institute at Harvard.

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
EntityNabla Bio — JAMmodel-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
OrganizationNabla Bio
Model family introducedNot normalized
AccessProprietary
Commercial useVendor terms
DeploymentVendor managed
ComputeManaged platform or GPU
Domainsantibody
Biology → representation → computation → evidence

How Nabla Bio — JAM represents biology

model-familyantibody

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

1 · Biological inputs
Antigen, sequence or desired properties
2 · Input representation
Sequence and/or antigen geometry
3 · Internal representation
Antibody representation
4 · Architecture
Antibody model
5 · Learning objective
Antibody prediction / design
6 · Output representation
SequenceStructureScores

Biological scale

Modalities & tasks

AntibodyProteinGenerationOptimizationPrediction

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

Antigen, sequence or desired properties

Outputs

Antibody candidatesAffinity or developability estimates

Scientific and technical profile

Scientific principles

De novo antibody designEpitope-specific generationAtomic modeling

Technology

Joint atomic generative modelDevelopability filters
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.

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: antibody, antigen, specificity
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, specificity
Structural biology

Anfinsen’s dogma—the thermodynamic hypothesis

Christian B. Anfinsen

Protein structure prediction, inverse folding and generative protein design all assume that sequence strongly constrains structure and function.

Matched concepts: sequence, protein
Biologics & genome engineering

Somatic gene rearrangement generates antibody diversity

Susumu Tonegawa

Antibody language models and repertoire design operate on the sequence space created by V(D)J recombination and somatic diversification.

Matched concepts: antibody, sequence
Biologics & genome engineering

Phage display and selection of binding proteins

George P. Smith & Sir Gregory P. Winter

Display-based selection created an experimental search engine for protein binders and remains a core validation partner for computational antibody design.

Matched concepts: antibody, affinity
Molecular recognition

Lock-and-key molecular recognition

Emil Fischer

The metaphor seeded structure-based ligand design, pharmacophore thinking and the search for complementary binding pockets.

Matched concepts: specificity

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

Not normalized

Targets multi-pass membrane proteins directly.

Evidence

Spun out of Harvard's Wyss Institute.