Ranked pose SDF and PDB files plus results.json with poseId, rank and score — why that score is pose accuracy within 2 Å, not affinity, and which to keep.

DiffDock returns ranked binding poses with a confidence score for each — the confidence tells you where the ligand binds, not how strongly. Use it to pick the top few poses worth minimizing with MM-GBSA. Note that MM-GBSA here returns no binding free energy, so neither tool answers how strongly the ligand binds.

Output artifacts

DiffDock generates multiple binding poses per ligand by sampling from a learned diffusion process. Each pose represents a complete ligand placement including position, orientation, and torsion angles. The poses are ranked by a confidence model that predicts the likelihood of each pose being close to the true binding mode.

Pose files: SDF for ligands and PDB for complexes; one record per pose.
results.json: structured metadata with poses and scores.
Visualization previews: quick-look images for the top-ranked poses (when enabled).

Key fields in `results.json`

poseId: unique ID per pose.
ligandId: your submitted ligand identifier (if provided).
rank: 1-based ordering by model confidence.
score: DiffDock confidence score (higher is better).

There's no reference-ligand input on this tool, so no RMSD-to-a-known-pose is computed or returned — validate a pose against a known binding mode by inspection instead.

Interpreting rankings

DiffDock's confidence model is trained to predict whether a pose is within 2 Angstroms RMSD of the true binding mode. How often the top-ranked pose is actually correct depends heavily on how close your target is to the structures the model was trained on — reported success rates range from 43% on standard benchmarks down to 12% on complexes no method had seen before. See how DiffDock results compare to the bench before treating a top pose as correct.

Focus on the top 3–5 poses for downstream inspection; quality typically drops after that.
Low scores across all poses often signal poor ligand geometry or an unsuitable protein preparation.
The confidence score reflects the model's estimate of pose accuracy, not binding affinity. DiffDock does not compute a binding energy, and no tool on this platform supplies one — MM-GBSA minimizes the complex and reports its energy but returns no binding free energy. A high-confidence pose is a geometric hypothesis; affinity needs an assay.

Filtering tips

Discard poses with severe clashes on quick visual inspection before scoring downstream.
If multiple poses share similar scores, prioritize those with better hydrogen-bonding networks and fewer strained torsions.
Consider running a physics-based minimization on the top poses to resolve minor clashes.

Canvas Visualization

When a DiffDock job completes, a result card appears on the canvas.

Card size: 400×450px
Border color: Purple (#9C27B0)
Visualization: Interactive 3D Mol* viewer with protein surface and ligand overlay

What You See

Confidence score (%) — model's estimate of pose correctness
Number of poses generated
Pose gallery to cycle through predicted binding modes

Interpreting Canvas Metrics

Metric	Meaning
High confidence	Pose is geometrically plausible — likely close to the true binding mode
Low confidence across all poses	Check ligand geometry or protein preparation; consider MM-GBSA to rescore

DiffDock does not report a binding affinity or energy value on the canvas, and no downstream tool here supplies one — MM-GBSA returns complex energies, not a binding free energy.

Confidence measures pose accuracy, not binding strength. A high-confidence pose tells you where the molecule binds, not how strongly.

DiffDock outputs and ranking