tooluniverse-network-pharmacology

name: tooluniverse-network-pharmacology description: Construct and analyze compound-target-disease networks for drug repurposing, polypharmacology discovery, and systems pharmacology. Builds multi-layer networks from ChEMBL, OpenTargets, STRING, DrugBank, Reactome, FAERS, and 60+ other ToolUniverse tools. Calculates Network Pharmacology Scores (0-100), identifies repurposing candidates, predicts mechanisms, and analyzes polypharmacology. Use when users ask about drug repurposing via network analysis, multi-target drug effects, compound-target-disease networks, systems pharmacology, or polypharmacology.

Network Pharmacology Pipeline

Construct and analyze compound-target-disease (C-T-D) networks to identify drug repurposing opportunities, understand polypharmacology, and predict drug mechanisms using systems pharmacology approaches.

IMPORTANT: Always use English terms in tool calls (drug names, disease names, target names), even if the user writes in another language. Respond in the user's language.

When to Use This Skill

Apply when users:

• Ask "Can [drug] be repurposed for [disease] based on network analysis?"
• Want to understand multi-target (polypharmacology) effects of a compound
• Need compound-target-disease network construction and analysis
• Ask about network proximity between drug targets and disease genes
• Want systems pharmacology analysis of a drug or target
• Need mechanism prediction for a drug in a new indication

NOT for (use other skills instead):

• Simple drug repurposing without network analysis -> tooluniverse-drug-repurposing
• Single target validation -> tooluniverse-drug-target-validation
• Adverse event detection only -> tooluniverse-adverse-event-detection

Network Pharmacology Score (0-100)

Tiers: 80-100 = Tier 1 (high potential) | 60-79 = Tier 2 (good) | 40-59 = Tier 3 (moderate) | 0-39 = Tier 4 (low)

Evidence grades: [T1] Human clinical proof | [T2] Functional experimental | [T3] Association/computational | [T4] Prediction/text-mining

KEY PRINCIPLES

1. Report-first approach - Create report file FIRST, then populate progressively
2. Entity disambiguation FIRST - Resolve all identifiers before analysis
3. Bidirectional network - Construct C-T-D network from both directions
4. Network metrics - Calculate proximity, centrality, module overlap quantitatively
5. Rank candidates - Prioritize by composite Network Pharmacology Score
6. Mechanism prediction - Explain HOW drug could work via network paths
7. Evidence grading - Grade all evidence T1-T4
8. Source references - Every finding must cite the source tool/database
9. Completeness checklist - Mandatory section at end showing analysis coverage

Complete Workflow

Phase 0: Entity Disambiguation + Report Setup

1. Create [entity]_network_pharmacology_report.md with all section headers
2. Resolve entities to IDs:

from tooluniverse import ToolUniverse
tu = ToolUniverse(use_cache=True); tu.load_tools()

# Compound -> ChEMBL ID
drug_info = tu.tools.OpenTargets_get_drug_chembId_by_generic_name(drugName="metformin")
chembl_id = drug_info['data']['search']['hits'][0]['id']

# Target -> Ensembl ID
target_info = tu.tools.OpenTargets_get_target_id_description_by_name(targetName="PSEN1")
ensembl_id = target_info['data']['search']['hits'][0]['id']

# Disease -> EFO ID
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName="Alzheimer disease")
disease_id = disease_info['data']['search']['hits'][0]['id']

Phase 1: Network Node Identification

Phase 2: Network Edge Construction

Phase 3: Network Analysis

1. Topology: Calculate degree centrality, betweenness, hub genes
2. Proximity: Shortest path distances between drug targets and disease genes → Z-score
3. Module overlap: Shared genes/pathways between drug module and disease module
4. Pathway enrichment: ReactomeAnalysis_pathway_enrichment (identifiers as space-separated string, NOT array), enrichr_gene_enrichment_analysis (gene_list + libs required)

Phase 4: Scoring & Ranking

1. For each drug-disease pair, compute the 5-component Network Pharmacology Score
2. Rank candidates by composite score
3. For top candidates, predict mechanism via network shortest path

Phase 5: Safety & Clinical Context

Key Tool API Notes

• DrugBank tools: ALL require 4 params: query, case_sensitive, exact_match, limit
• FAERS analytics tools: ALL require operation parameter
• FAERS count tools: Use medicinalproduct NOT drug_name
• OpenTargets: Returns nested {data: {entity: {field: ...}}}
• ReactomeAnalysis: identifiers must be space-separated string, NOT list
• STRING: species=9606 for human, protein_ids as list
• ensembl_lookup_gene: species='homo_sapiens' REQUIRED

Report Template (10 sections)

1. Executive Summary (score, tier, recommendation)
2. Network Construction (nodes + edges counts, data sources)
3. Network Proximity (Z-score, direct interactions, shared PPI, shared pathways)
4. Top Repurposing Candidates (ranked by score with mechanism prediction)
5. Polypharmacology Profile (target coverage, multi-target effects)
6. Pathway Analysis (drug pathways, disease pathways, overlap = mechanism)
7. Safety Considerations (AEs, target safety flags, off-target risks)
8. Clinical Precedent (trials, literature, PGx)
9. Evidence Summary Table (finding, source, grade, confidence)
10. Completeness Checklist (phase-by-phase status)

Extended Reference: Full code examples for each phase, complete tool parameter tables with response structures, and the full report template are in REFERENCE.md.