Why this evidence?
It is linked to a specific model version, scientific task, dataset, split, metric and source. That makes the claim inspectable rather than a detached marketing score.
IgLM / AntiBERTy: Antibody sequence modelling
Peer-reviewed antibody language-modelling evaluation.
Model versionVersion history not yet curated
TaskAntibody sequence modelling
DatasetAntibody sequence evaluations
SplitAntibody sequence evaluation sets
MetricAntibody language modelling
Replicationemerging
Reported byModel developers
Review statuscurated
Evidence confidence · limited
Confidence is multidimensional, not a universal model score.
Evidence completenessstrongModel version, task, dataset, split, metric, source and provenance fields.
Independent validationlimitedemerging
Source qualitystrongPeer-reviewed
Reproducibility evidencelimitedReflects documented replication status, not a universal reproducibility score.
Version specificitystrongVersion history not yet curated
Context applicabilitystrongDepends on task, split and explicit caveats; users must still validate their own context.
Contradiction reviewclearNo direct contradiction signal is currently queued.
BioAtlas reports evidence dimensions separately so a strong source cannot hide weak applicability, incomplete replication or unresolved contradiction.
Why?
Why should this evidence influence a decision?
Why not a universal score?
Performance can change with dataset, split, preprocessing, metric and context of use. BioAtlas therefore keeps confidence dimensions separate.
What could change the conclusion?
Independent replication, a better matched prospective dataset, a version change, a contradictory result or a more relevant validation protocol can reopen this evidence record.
Evidence boundary
What this claim does not prove.
- Sequence plausibility does not guarantee affinity, specificity or developability.
- Training-set lineage and germline distribution affect generalization.
BioAtlas groups benchmark claims only when task, dataset, split, metric and protocol context align. This record is not a universal model score.
Model context
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.
Full evidence passport →Known model 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.