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ZIP herunterladen Herunterladen... Mehr aus diesem Repository 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.
Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
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"]
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 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 --modeYes Pipeline mode: full, lead-opt, screen, assess, denovo --proteinYes* Input PDB file (*or --sequence) --sequenceNo Protein sequence (triggers structure prediction if no PDB) --ligandlead-opt Input ligand SDF file --libraryscreen Compound library SDF file --pocketNo Pre-computed pocket JSON (skips pocket detection) --output-dirNo Output directory (default: ./pipeline_results/) --skipNo Comma-separated stages to skip --top-nNo Number of top compounds (default: 10) --docking-methodNo 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 visualizationbinding-affinity : Standalone affinity prediction and consensus scoringdenovo-design : Standalone molecule generation with multiple strategiesmolecular-docking : Standalone docking with Vina/DiffDockstructure-prediction : Standalone protein structure prediction from sequencemolecule-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).
