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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.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/synthetic-sciences/openscience --skill drug-designコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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.
Fast inference and fine-tuning platform with serverless and on-demand GPU deployments. OpenAI-compatible API for chat completions, embeddings, function calling, vision, and structured output. Supports SFT, DPO, and RL fine-tuning. SOC2 + HIPAA compliant.
Serverless inference, fine-tuning, embeddings, image generation, and batch processing on 200+ open-source models via an OpenAI-compatible API. Use when you need fast, cost-effective access to open-source LLMs without managing infrastructure.
SOC 職業分類に基づく
SKILL.md を表示中
| name | drug-design |
| 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. |
| category | chemistry |
| license | MIT |
| metadata | {"skill-author":"Synthetic Sciences"} |
| version | 1.0.0 |
| author | Synthetic Sciences |
| tags | ["Drug Discovery","Pipeline","Orchestration","Drug Design"] |
| dependencies | ["biopython>=1.84","rdkit-pypi","numpy","scipy"] |
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:
_script_manifest.jsonl — critique agent can verify the full traceChoose 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"
python scripts/pipeline.py \
--mode full \
--protein target.pdb \
--output-dir results/ \
--top-n 10
python scripts/pipeline.py \
--mode lead-opt \
--protein target.pdb \
--ligand hit_compound.sdf \
--output-dir lead_opt_results/ \
--top-n 20
python scripts/pipeline.py \
--mode screen \
--protein target.pdb \
--library compound_library.sdf \
--output-dir screening_results/ \
--top-n 50
python scripts/pipeline.py \
--mode assess \
--protein target.pdb \
--output-dir assessment/
python scripts/pipeline.py \
--mode denovo \
--protein target.pdb \
--output-dir denovo_results/ \
--top-n 15
python scripts/pipeline.py \
--mode full \
--sequence "MKTLLLTLLLGLLVSSALA..." \
--output-dir results/
--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 |
--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 |
--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 |
--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 |
--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 |
These are the JSON schemas each stage expects from its upstream stage:
{
"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.
{
"predictions": [
{
"pose_id": 1,
"predicted_pKd": 7.2,
"predicted_dG_kcal": -9.8,
"confidence": "moderate"
}
]
}
{
"rankings": [
{
"pose_id": 1,
"consensus_score": 0.85,
"consensus_rank": 1,
"individual_ranks": {"predict": 1, "rescore": 2}
}
]
}
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)
| 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 | 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('...'))" |