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Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
Passed 20 of 21 test cases with the skill, and 7 of 21 without it.
and 14 more in the full report
14 fixed7 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to offer a local interactive web interface for team members who prefer not to use the CLI for DiffDock docking runs. What command launches the built-in web GUI application?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies executing python app/main.py. | Missed | Passed |
| The response states that the web interface runs at http://localhost:7860. | Passed | Passed |
The instructions your model reads when the skill loads.
------|------------------|----------------| | > 0 | High | Strong prediction, likely accurate | | -1.5 to 0 | Moderate | Reasonable prediction, validate carefully | | < -1.5 | Low | Uncertain prediction, requires validation |
Critical Notes:
For detailed guidance: Read references/confidence_and_limitations.md using the Read tool
Create custom configuration for specific use cases:
# Copy template
cp assets/custom_inference_config.yaml my_config.yaml
# Edit parameters (see template for presets)
# Then run with custom config
python -m inference \
--config my_config.yaml \
--protein_ligand_csv input.csv \
--out_dir results/
Sampling Density:
samples_per_complex: 10 → Increase to 20-40 for difficult casesInference Steps:
test cases passed, vs 7 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.