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name: results-analysis description: "Inspect BindCraft output folders and CSVs, explain filter outcomes and structure metrics, rank accepted designs conservatively, and recover from reporting or scoring failures." disable-model-invocation: true metadata: disco-role: operating license: MIT

Analyze BindCraft results

Use this route after a BindCraft run, partial run, or resumed run when the question is what was produced, why designs were rejected, or which accepted structures deserve review. Start with the output directory named by the target settings; do not assume it is inside the BindCraft checkout.

Fast route

1. Run the read-only results summarizer on the output directory, then inspect the CSV/PDB counts it reports.
2. Read output artifacts to reconcile trajectory_stats.csv, mpnn_design_stats.csv, final_design_stats.csv, failure_csv.csv, and the corresponding PDB folders.
3. Use metrics and scoring to compare designs across AF2, MPNN, Rosetta, interface, clash, secondary-structure, and RMSD evidence. Rank by Average_i_pTM only as BindCraft's structural proxy, never as an affinity measurement.
4. If artifacts disagree or a metric is absent, follow troubleshooting before deleting files or changing filters.

Applicability and boundaries

• This route can summarize existing files without PyRosetta, CUDA, AF2 weights, DSSP, or DAlphaBall. Metric recomputation may require those dependencies.
• It does not author target JSON or choose GPU/SLURM resources. For input structure and chain/hotspot questions, use target preparation; for launching, stopping, or resource setup, use design pipeline.
• Treat a complete-looking CSV as evidence of recorded processing, not proof that a full campaign finished. A skipped or interrupted run is not verified by inspection alone.
• Report the exact output path, CSV used, row/PDB correspondence, filter preset, and missing prerequisites in any handoff. Keep source checkout paths and verification artifacts out of user-facing runtime instructions.

Selection discipline

Use final_design_stats.csv and Accepted/Ranked/ when ranking has completed; otherwise use the available MPNN rows and Accepted/ PDBs, explicitly labeling that view as provisional. Prefer several complementary metrics and inspect the actual relaxed PDBs. Do not infer experimental affinity, specificity, expressibility, or success from AF2 confidence, Rosetta energy, or a filter pass.

The bundled script is deliberately read-only and uses only Python's standard library. It is a triage aid, not a replacement for structural inspection or a new scoring implementation.