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.
Uni-Mol / Uni-Mol2: Protein–ligand pose prediction
A structured benchmark claim is recorded; consult the linked source for numeric values and protocol details.
Model versionVersion history not yet curated
TaskProtein–ligand pose prediction
DatasetMolecular representation/property benchmarks
SplitSplit details not yet normalized
Metric3D molecular representation
Replicationunknown
Reported bySource authors
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 validationunknownunknown
Source qualitylimitedOpen evaluation
Reproducibility evidenceunknownReflects documented replication status, not a universal reproducibility score.
Version specificitystrongVersion history not yet curated
Context applicabilitymoderateDepends 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.
- Protocol, split and implementation details must match before comparing this claim with another result.
BioAtlas groups benchmark claims only when task, dataset, split, metric and protocol context align. This record is not a universal model score.
Model context
Uni-Mol and Uni-Mol2 learn transferable representations from 3D molecular conformers for chemistry prediction and structure-aware tasks.
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.