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.

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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

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Network Pharmacology Score (0-100)

Component	Max Points	Criteria
Network Proximity	35	Z<-2, p<0.01 = 35pts; Z<-1, p<0.05 = 20pts; Z<-0.5 = 10pts
Clinical Evidence	25	Approved related = 25; Active trials = 15; Completed = 10; Preclinical = 5
Target-Disease Association	20	GWAS/rare variants = 20; Pathway/literature = 12; Computational = 5
Safety Profile	10	FDA-approved favorable = 10; Manageable AEs = 7; Significant concerns = 3
Mechanism Plausibility	10	Clear pathway + functional = 10; Indirect via neighbors = 6; Computational = 2

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

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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

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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

Node Type	Primary Tool	Fallback
Compound targets	OpenTargets_get_drug_mechanisms_of_action_by_chemblId	drugbank_get_targets_by_drug_name_or_drugbank_id
Disease genes	OpenTargets_get_associated_targets_by_disease_efoId	CTD_get_gene_diseases
PPI partners	STRING_get_interaction_partners (species=9606)	OpenTargets_get_target_interactions_by_ensemblID

Phase 2: Network Edge Construction

Edge Type	Tools
C-T (drug-target)	OpenTargets_get_drug_mechanisms_of_action_by_chemblId, DGIdb_get_drug_gene_interactions, CTD_get_chemical_gene_interactions
T-D (target-disease)	OpenTargets_get_associated_targets_by_disease_efoId, OpenTargets_target_disease_evidence, GWAS_search_associations_by_gene
C-D (drug-disease)	OpenTargets_get_drug_indications_by_chemblId, search_clinical_trials, CTD_get_chemical_diseases
T-T (PPI)	STRING_get_interaction_partners, STRING_get_network, intact_search_interactions

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

Tool	Purpose
FAERS_calculate_disproportionality	AE signal detection (PRR, ROR)
FAERS_count_death_related_by_drug	Serious outcomes (medicinalproduct, NOT drug_name)
OpenTargets_get_drug_adverse_events_by_chemblId	Known AEs
OpenTargets_get_target_safety_profile_by_ensemblID	Target safety
search_clinical_trials	Existing trials (query_term REQUIRED)
PubMed_search_articles	Literature (returns plain list, NOT {articles: [...]})

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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

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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)

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