What it is
IgLM is a generative language model for antibody sequences enabling controllable design and humanization, while AntiBERTy provides antibody representations for downstream tasks. Together they helped establish antibody-specific language modeling and underpin tools like the IgFold structure predictor.
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.
Model passport
How IgLM / AntiBERTy represents biology
Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.
Biological scale
Modalities & tasks
Registry, claims and frontier intelligence
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 →1 normalized claim
Antibody sequence modelling · Antibody sequence evaluations
Open claim intelligence →0 connected frontiers
No frontier-research record currently connects to this model.
Inspect research horizon →Inputs and outputs
Inputs
Antigen, sequence or desired propertiesOutputs
Antibody candidatesAffinity or developability estimatesScientific and technical profile
Scientific principles
Technology
Scientific lineage
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.
Phage display and selection of binding proteins
George P. Smith & Sir Gregory P. WinterDisplay-based selection created an experimental search engine for protein binders and remains a core validation partner for computational antibody design.
Anfinsen’s dogma—the thermodynamic hypothesis
Christian B. AnfinsenProtein structure prediction, inverse folding and generative protein design all assume that sequence strongly constrains structure and function.
Hybridoma production of monoclonal antibodies
Georges J. F. Köhler & César MilsteinTherapeutic antibodies, diagnostic antibodies and antibody discovery platforms became scalable and reproducible.
Somatic gene rearrangement generates antibody diversity
Susumu TonegawaAntibody language models and repertoire design operate on the sequence space created by V(D)J recombination and somatic diversification.
Transformer self-attention
Ashish Vaswani and colleaguesProtein, genome, molecule and single-cell foundation models use attention to learn dependencies across biological sequences and multimodal inputs.
Information, entropy and communication
Claude E. ShannonSequence modelling, cross-entropy training, language models, mutual information and representation learning all use Shannon’s framework.
Evaluation evidence
Task-specific evidence only; not comparable as a universal leaderboard score.
Antibody sequence evaluations
Version history not yet curated · Antibody sequence evaluation setsPeer-reviewed antibody language-modelling evaluation.
Claim caveats
- Sequence plausibility does not guarantee affinity, specificity or developability.
- Training-set lineage and germline distribution affect generalization.
Known limitations
- Performance depends on the evaluation dataset and operating conditions.
- Task-specific benchmark results should not be compared across unlike domains.
- Outputs require task-specific scientific and experimental validation.
Milestones
Underpins the IgFold antibody structure predictor.
Openly released for research.