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.
76
66%
Does it follow best practices?
Impact
94%
1.54xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/diffdock/SKILL.mdBatch virtual screening pipeline
Batch CSV validation script
0%
100%
Correct CSV column names
0%
100%
Results analysis script usage
0%
100%
Export to CSV
0%
100%
ESM embedding precomputation
0%
0%
Confidence is not affinity
100%
100%
Confidence score thresholds
80%
100%
Downstream scoring recommendation
100%
100%
Canonical SMILES in CSV
100%
100%
Batch size parameter
100%
100%
Custom configuration for challenging docking
Config from template
50%
100%
Increased torsion temperature
0%
100%
Increased samples per complex
100%
100%
Increased inference steps
100%
100%
Ensemble CSV for both conformations
53%
100%
Uses --protein_ligand_csv flag
87%
100%
Torsion temperature rationale
0%
100%
Samples rationale
100%
100%
No affinity claims
100%
100%
Results interpretation and affinity pipeline
Uses analyze_results.py
0%
100%
Three-tier classification
100%
100%
Correct High confidence compounds
100%
100%
Correct Low confidence compounds
100%
100%
Confidence is not potency
100%
100%
Large peptide flag
100%
100%
Large ligand caveat
100%
100%
Downstream affinity tool
100%
100%
Review multiple top poses
37%
50%
Export to CSV
100%
100%
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Table of Contents
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