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Zhongwei Li
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skills/tooluniverse/scripts/example_workflow.py
Executable file
219
skills/tooluniverse/scripts/example_workflow.py
Executable file
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#!/usr/bin/env python3
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"""
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Example workflow demonstrating tool composition in ToolUniverse.
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This script shows a complete drug discovery workflow:
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1. Find disease-associated targets
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2. Retrieve protein sequences
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3. Get structure predictions
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4. Screen compound libraries
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5. Calculate drug-likeness properties
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"""
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from tooluniverse import ToolUniverse
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def drug_discovery_workflow(disease_efo_id: str, max_targets: int = 3):
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"""
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Execute a drug discovery workflow for a given disease.
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Args:
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disease_efo_id: EFO ID for the disease (e.g., "EFO_0000537" for hypertension)
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max_targets: Maximum number of targets to process
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"""
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tu = ToolUniverse()
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tu.load_tools()
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print("=" * 70)
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print("DRUG DISCOVERY WORKFLOW")
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print("=" * 70)
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# Step 1: Find disease-associated targets
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print(f"\nStep 1: Finding targets for disease {disease_efo_id}...")
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targets = tu.run({
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"name": "OpenTargets_get_associated_targets_by_disease_efoId",
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"arguments": {"efoId": disease_efo_id}
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})
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print(f"✓ Found {len(targets)} disease-associated targets")
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# Process top targets
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top_targets = targets[:max_targets]
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print(f" Processing top {len(top_targets)} targets:")
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for idx, target in enumerate(top_targets, 1):
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print(f" {idx}. {target.get('target_name', 'Unknown')} ({target.get('uniprot_id', 'N/A')})")
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# Step 2: Get protein sequences
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print(f"\nStep 2: Retrieving protein sequences...")
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sequences = []
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for target in top_targets:
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try:
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seq = tu.run({
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"name": "UniProt_get_sequence",
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"arguments": {"uniprot_id": target['uniprot_id']}
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})
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sequences.append({
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"target": target,
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"sequence": seq
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})
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print(f" ✓ Retrieved sequence for {target.get('target_name', 'Unknown')}")
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except Exception as e:
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print(f" ✗ Failed to get sequence: {e}")
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# Step 3: Predict protein structures
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print(f"\nStep 3: Predicting protein structures...")
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structures = []
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for seq_data in sequences:
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try:
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structure = tu.run({
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"name": "AlphaFold_get_structure",
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"arguments": {"uniprot_id": seq_data['target']['uniprot_id']}
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})
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structures.append({
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"target": seq_data['target'],
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"structure": structure
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})
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print(f" ✓ Predicted structure for {seq_data['target'].get('target_name', 'Unknown')}")
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except Exception as e:
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print(f" ✗ Failed to predict structure: {e}")
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# Step 4: Find binding sites
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print(f"\nStep 4: Identifying binding sites...")
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binding_sites = []
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for struct_data in structures:
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try:
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sites = tu.run({
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"name": "Fpocket_find_binding_sites",
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"arguments": {"structure": struct_data['structure']}
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})
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binding_sites.append({
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"target": struct_data['target'],
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"sites": sites
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})
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print(f" ✓ Found {len(sites)} binding sites for {struct_data['target'].get('target_name', 'Unknown')}")
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except Exception as e:
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print(f" ✗ Failed to find binding sites: {e}")
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# Step 5: Virtual screening (simplified)
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print(f"\nStep 5: Screening compound libraries...")
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all_hits = []
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for site_data in binding_sites:
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for site in site_data['sites'][:1]: # Top site only
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try:
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compounds = tu.run({
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"name": "ZINC_virtual_screening",
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"arguments": {
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"binding_site": site,
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"library": "lead-like",
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"top_n": 10
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}
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})
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all_hits.extend(compounds)
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print(f" ✓ Found {len(compounds)} hit compounds for {site_data['target'].get('target_name', 'Unknown')}")
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except Exception as e:
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print(f" ✗ Screening failed: {e}")
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# Step 6: Calculate drug-likeness
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print(f"\nStep 6: Evaluating drug-likeness...")
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drug_candidates = []
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for compound in all_hits:
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try:
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properties = tu.run({
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"name": "RDKit_calculate_drug_properties",
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"arguments": {"smiles": compound['smiles']}
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})
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if properties.get('lipinski_pass', False):
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drug_candidates.append({
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"compound": compound,
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"properties": properties
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})
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except Exception as e:
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print(f" ✗ Property calculation failed: {e}")
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print(f"\n ✓ Identified {len(drug_candidates)} drug candidates passing Lipinski's Rule of Five")
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# Summary
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print("\n" + "=" * 70)
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print("WORKFLOW SUMMARY")
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print("=" * 70)
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print(f"Disease targets processed: {len(top_targets)}")
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print(f"Protein structures predicted: {len(structures)}")
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print(f"Binding sites identified: {sum(len(s['sites']) for s in binding_sites)}")
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print(f"Compounds screened: {len(all_hits)}")
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print(f"Drug candidates identified: {len(drug_candidates)}")
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print("=" * 70)
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return drug_candidates
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def genomics_workflow(geo_id: str):
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"""
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Execute a genomics analysis workflow.
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Args:
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geo_id: GEO dataset ID (e.g., "GSE12345")
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"""
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tu = ToolUniverse()
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tu.load_tools()
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print("=" * 70)
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print("GENOMICS ANALYSIS WORKFLOW")
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print("=" * 70)
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# Step 1: Download gene expression data
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print(f"\nStep 1: Downloading dataset {geo_id}...")
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try:
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expression_data = tu.run({
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"name": "GEO_download_dataset",
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"arguments": {"geo_id": geo_id}
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})
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print(f" ✓ Downloaded expression data")
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except Exception as e:
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print(f" ✗ Failed: {e}")
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return
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# Step 2: Differential expression analysis
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print(f"\nStep 2: Performing differential expression analysis...")
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try:
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de_genes = tu.run({
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"name": "DESeq2_differential_expression",
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"arguments": {
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"data": expression_data,
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"condition1": "control",
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"condition2": "treated"
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}
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})
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print(f" ✓ Found {len(de_genes.get('significant_genes', []))} differentially expressed genes")
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except Exception as e:
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print(f" ✗ Failed: {e}")
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return
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# Step 3: Pathway enrichment
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print(f"\nStep 3: Running pathway enrichment analysis...")
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try:
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pathways = tu.run({
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"name": "KEGG_pathway_enrichment",
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"arguments": {
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"gene_list": de_genes['significant_genes'],
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"organism": "hsa"
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}
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})
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print(f" ✓ Found {len(pathways)} enriched pathways")
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if pathways:
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print(f" Top pathway: {pathways[0].get('pathway_name', 'Unknown')}")
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except Exception as e:
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print(f" ✗ Failed: {e}")
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print("\n" + "=" * 70)
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if __name__ == "__main__":
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# Example 1: Drug discovery workflow for hypertension
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print("EXAMPLE 1: Drug Discovery for Hypertension")
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candidates = drug_discovery_workflow("EFO_0000537", max_targets=2)
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print("\n\n")
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# Example 2: Genomics workflow
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print("EXAMPLE 2: Genomics Analysis")
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genomics_workflow("GSE12345")
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