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.
RFdiffusion: De novo protein or binder design
The primary publication includes experimental validation of generated designs.
Model versionRFdiffusion2
TaskDe novo protein or binder design
DatasetExperimental binder validation
SplitProspective designed proteins and binders
MetricDe novo protein design
Replicationexperimental-primary
Reported byModel developers with wet-lab validation
Review statuscurated
Evidence confidence · strong
Confidence is multidimensional, not a universal model score.
Evidence completenessstrongModel version, task, dataset, split, metric, source and provenance fields.
Independent validationstrongexperimental-primary
Source qualitystrongPeer-reviewed + experimental
Reproducibility evidencestrongReflects documented replication status, not a universal reproducibility score.
Version specificitystrongRFdiffusion2
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.
- Experimental success rates depend on design objective and filtering pipeline.
- A generated backbone still requires sequence design and laboratory confirmation.
BioAtlas groups benchmark claims only when task, dataset, split, metric and protocol context align. This record is not a universal model score.
Model context
RFdiffusion applies denoising diffusion to protein backbones, generating novel folds, binders, symmetric assemblies and scaffolds around functional motifs. Paired with ProteinMPNN for sequence design, it is the workhorse of modern de novo protein design and lab-validated binders.
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.