Agent Skills
› synthetic-sciences/openscience
› drug-design
drug-design
GitHub端到端药物发现流程编排器，自动串联结构预测、口袋检测、从头设计、对接及ADMET筛选等步骤。通过确定性脚本确保执行顺序、验证数据契约并记录日志，支持全链路、先导物优化、虚拟筛选等多种模式。
Trigger Scenarios
drug discovery pipeline
find drugs
design drugs
screen compounds
druggability assessment
de novo design
lead optimization
Install
npx skills add synthetic-sciences/openscience --skill drug-design -g -y
SKILL.md
Frontmatter
{
"name": "drug-design",
"tags": [
"Drug Discovery",
"Pipeline",
"Orchestration",
"Drug Design"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "chemistry",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.",
"dependencies": [
"biopython>=1.84",
"rdkit-pypi",
"numpy",
"scipy"
]
}
Drug Design Pipeline
Overview
This skill provides a deterministic pipeline orchestrator (pipeline.py) that auto-chains multiple drug discovery skills into reproducible workflows. Instead of manually invoking 10+ scripts in the correct order, the agent runs a single command that handles file wiring, schema validation, and manifest logging at every stage.
Why a script instead of manual chaining:
- Guarantees correct execution order — the agent cannot skip or reorder stages
- Validates I/O contracts between stages — catches schema mismatches early
- Logs every invocation to
_script_manifest.jsonl— critique agent can verify the full trace - Stops on first failure with clear diagnostics — no silent errors
Pipeline Mode Selection
Choose the mode based on what the user wants:
| User Intent | Mode | Command |
|---|---|---|
| "Find drugs for target X" | full |
--mode full --protein target.pdb |
| "Optimize this hit compound" | lead-opt |
--mode lead-opt --protein target.pdb --ligand hit.sdf |
| "Screen this library" | screen |
--mode screen --protein target.pdb --library compounds.sdf |
| "Is this target druggable?" | assess |
--mode assess --protein target.pdb |
| "Design molecules for this pocket" | denovo |
--mode denovo --protein target.pdb |
Trigger phrases: "drug discovery pipeline", "find drugs", "design drugs", "screen compounds", "druggability assessment", "de novo design", "lead optimization"
Quick Start
Full Pipeline (target → drug candidates)
python scripts/pipeline.py \
--mode full \
--protein target.pdb \
--output-dir results/ \
--top-n 10
Lead Optimization (improve an existing hit)
python scripts/pipeline.py \
--mode lead-opt \
--protein target.pdb \
--ligand hit_compound.sdf \
--output-dir lead_opt_results/ \
--top-n 20
Virtual Screening (screen a compound library)
python scripts/pipeline.py \
--mode screen \
--protein target.pdb \
--library compound_library.sdf \
--output-dir screening_results/ \
--top-n 50
Target Assessment (is this druggable?)
python scripts/pipeline.py \
--mode assess \
--protein target.pdb \
--output-dir assessment/
De Novo Design (generate novel molecules)
python scripts/pipeline.py \
--mode denovo \
--protein target.pdb \
--output-dir denovo_results/ \
--top-n 15
From Sequence (no PDB available)
python scripts/pipeline.py \
--mode full \
--sequence "MKTLLLTLLLGLLVSSALA..." \
--output-dir results/
Pipeline Stages Reference
Full Pipeline (--mode full)
| Stage | Skill | Script | Input | Output |
|---|---|---|---|---|
| 1. Structure Prediction | structure-prediction | predict.py | Sequence | predicted_structure.pdb |
| 2. Pocket Detection | pocket-detection | detect.py | PDB | pockets.json |
| 3. Druggability | pocket-detection | druggability.py | PDB + pockets.json | druggability.json |
| 4. De Novo Design | denovo-design | generate_sbdd.py | PDB + pockets.json | candidates.sdf |
| 5. Drug-Likeness Filter | denovo-design | filter.py | candidates.sdf | filtered.sdf |
| 6. Docking | molecular-docking | dock.py | PDB + filtered.sdf | docking/poses.sdf |
| 7. Interaction Scoring | molecular-docking | score.py | PDB + poses.sdf | interactions.json |
| 8. Affinity Prediction | binding-affinity | predict.py | PDB + poses.sdf | affinity.json |
| 9. MM/GBSA Rescore | binding-affinity | rescore.py | PDB + poses.sdf | mmgbsa.json |
| 10. Consensus Ranking | binding-affinity | consensus.py | All score files | consensus.json |
| 11. 3D Visualization | molecule-visualization | render_3d.py | PDB + poses.sdf | complex_3d.html |
Lead Optimization (--mode lead-opt)
| Stage | Script | Input | Output |
|---|---|---|---|
| 1. Analog Generation | generate_analogs.py | hit.sdf | analogs.sdf |
| 2. Drug-Likeness Filter | filter.py | analogs.sdf | filtered.sdf |
| 3. Docking | dock.py | PDB + filtered.sdf | docking/poses.sdf |
| 4. Affinity Prediction | predict.py | PDB + poses.sdf | affinity.json |
| 5. Consensus Ranking | consensus.py | affinity.json | consensus.json |
Virtual Screening (--mode screen)
| Stage | Script | Input | Output |
|---|---|---|---|
| 1. Pocket Detection | detect.py | PDB | pockets.json |
| 2. Batch Scoring | batch.py | PDB + library.sdf | screening_hits.csv |
| 3. Docking | dock.py | PDB + hits | docking/poses.sdf |
| 4. Affinity Prediction | predict.py | PDB + poses.sdf | affinity.json |
| 5. Consensus Ranking | consensus.py | affinity.json | consensus.json |
Target Assessment (--mode assess)
| Stage | Script | Input | Output |
|---|---|---|---|
| 1. Structure Prediction | predict.py | Sequence | predicted.pdb |
| 2. Pocket Detection | detect.py | PDB | pockets.json |
| 3. Druggability | druggability.py | PDB + pockets.json | druggability.json |
| 4. Summary Plot | visualize.py | PDB + druggability.json | pocket_summary.png |
| 5. Druggability Radar | visualize.py | PDB + druggability.json | druggability_radar.png |
| 6. 3D View | render_3d.py | PDB | protein_3d.html |
De Novo Design (--mode denovo)
| Stage | Script | Input | Output |
|---|---|---|---|
| 1. Pocket Detection | detect.py | PDB | pockets.json |
| 2. SBDD Generation | generate_sbdd.py | PDB + pockets.json | candidates_sbdd.sdf |
| 3. Fragment Generation | generate_fragments.py | PDB + pockets.json | candidates_frag.sdf |
| 4. Drug-Likeness Filter | filter.py | candidates.sdf | filtered.sdf |
| 5. Docking | dock.py | PDB + filtered.sdf | docking/poses.sdf |
| 6. Affinity Prediction | predict.py | PDB + poses.sdf | affinity.json |
| 7. Consensus Ranking | consensus.py | affinity.json | consensus.json |
I/O Contract Reference
These are the JSON schemas each stage expects from its upstream stage:
pockets.json (pocket-detection → docking, druggability, denovo)
{
"pockets": [
{
"center": [10.5, 22.3, 15.0],
"volume_A3": 542.8,
"residues": ["ASP189", "SER195"]
}
]
}
Critical field: pockets[0].center must be [float, float, float] — dock.py reads this directly.
affinity.json (binding-affinity → consensus)
{
"predictions": [
{
"pose_id": 1,
"predicted_pKd": 7.2,
"predicted_dG_kcal": -9.8,
"confidence": "moderate"
}
]
}
consensus.json (final output)
{
"rankings": [
{
"pose_id": 1,
"consensus_score": 0.85,
"consensus_rank": 1,
"individual_ranks": {"predict": 1, "rescore": 2}
}
]
}
Output Directory Structure
After --mode full:
pipeline_results/
├── pockets.json # Detected binding pockets
├── druggability.json # Pocket druggability scores
├── candidates.sdf # Generated molecules (pre-filter)
├── filtered.sdf # Drug-like molecules (post-filter)
├── docking/
│ ├── poses.sdf # Docked poses
│ └── scores.csv # Docking scores
├── interactions.json # Protein-ligand interactions
├── affinity.json # Binding affinity predictions
├── mmgbsa.json # MM/GBSA rescoring
├── consensus.json # Final consensus ranking
├── complex_3d.html # Interactive 3D viewer
├── pipeline_report.json # Stage timings and status
└── _script_manifest.jsonl # Full invocation log (for critique)
Script Reference
| Argument | Required | Description |
|---|---|---|
--mode |
Yes | Pipeline mode: full, lead-opt, screen, assess, denovo |
--protein |
Yes* | Input PDB file (*or --sequence) |
--sequence |
No | Protein sequence (triggers structure prediction if no PDB) |
--ligand |
lead-opt | Input ligand SDF file |
--library |
screen | Compound library SDF file |
--pocket |
No | Pre-computed pocket JSON (skips pocket detection) |
--output-dir |
No | Output directory (default: ./pipeline_results/) |
--skip |
No | Comma-separated stages to skip |
--top-n |
No | Number of top compounds (default: 10) |
--docking-method |
No | Docking engine: vina or diffdock (default: vina) |
Error Recovery
| Error | Cause | Fix |
|---|---|---|
| "Script not found" | Missing skill or wrong OPENSCIENCE_SKILLS_DIR | Set OPENSCIENCE_SKILLS_DIR to skills root or ensure skills are installed |
| "Schema validation failed" | Upstream script produced unexpected output | Check the failed stage's output file manually |
| "No pockets detected" | Protein too small or no clear cavity | Try --skip pocket-detection with manual --pocket coordinates |
| "Docking failed" | Missing Vina binary or wrong PDB format | Install Vina: pip install vina, or use --docking-method diffdock |
| "No analogs generated" | Input ligand too complex or invalid SMILES | Check ligand parses in RDKit: python -c "from rdkit import Chem; print(Chem.MolFromSmiles('...'))" |
Related Skills
- pocket-detection: Standalone pocket detection with visualization
- binding-affinity: Standalone affinity prediction and consensus scoring
- denovo-design: Standalone molecule generation with multiple strategies
- molecular-docking: Standalone docking with Vina/DiffDock
- structure-prediction: Standalone protein structure prediction from sequence
- molecule-visualization: Standalone 2D/3D molecular visualization
References
- Eberhardt, J. et al. "AutoDock Vina 1.2.0." J. Chem. Inf. Model. 61, 3891-3898 (2021).
- Corso, G. et al. "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking." ICLR (2023).
- Le Guilloux, V. et al. "Fpocket." BMC Bioinformatics 10, 168 (2009).
- Wang, R. et al. "The PDBbind database." J. Med. Chem. 47, 2977-2980 (2004).
Version History
- e9844a4 Current 2026-07-11 17:21
Dependencies
-
required
biopython>=1.84 -
required
rdkit-pypi -
required
numpy -
required
scipy
