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. Only try original-language terms as a fallback if English returns no results. 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
• Ask about drug repurposing candidates ranked by network metrics
• Need mechanism prediction for a drug in a new indication
• Want to identify hub genes in disease networks as therapeutic targets
• Ask about disease module coverage by a compound's targets

NOT for (use other skills instead):

• Simple drug repurposing without network analysis -> Use tooluniverse-drug-repurposing
• Single target validation -> Use tooluniverse-drug-target-validation
• Adverse event detection only -> Use tooluniverse-adverse-event-detection
• General disease research -> Use tooluniverse-disease-research
• GWAS interpretation -> Use tooluniverse-gwas-snp-interpretation

Input Parameters

Network Pharmacology Score (0-100)

Priority Tiers

Evidence Grading

Full scoring details: SCORING_REFERENCE.md

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 comprehensively 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 for disease via network paths
7. Clinical feasibility - FDA-approved drugs ranked higher than preclinical
8. Safety context - Flag known adverse events and off-target liabilities
9. Evidence grading - Grade all evidence T1-T4
10. Negative results documented - "No data" is data; empty sections are failures
11. Source references - Every finding must cite the source tool/database
12. Completeness checklist - Mandatory section at end showing analysis coverage

Workflow Overview

Phase 0: Entity Disambiguation and Report Setup

• Create report file immediately
• Resolve entity to all required IDs (ChEMBL, DrugBank, PubChem CID, Ensembl, MONDO/EFO)
• Tools: OpenTargets_get_drug_chembId_by_generic_name, drugbank_get_drug_basic_info_by_drug_name_or_id, PubChem_get_CID_by_compound_name, OpenTargets_get_target_id_description_by_name, OpenTargets_get_disease_id_description_by_name

Phase 1: Network Node Identification

• Compound nodes: Drug targets, mechanism of action, current indications
• Target nodes: Disease-associated genes, GWAS targets, druggability levels
• Disease nodes: Related diseases, hierarchy, phenotypes
• Tools: OpenTargets_get_drug_mechanisms_of_action_by_chemblId, OpenTargets_get_associated_targets_by_drug_chemblId, drugbank_get_targets_by_drug_name_or_drugbank_id, DGIdb_get_drug_gene_interactions, CTD_get_chemical_gene_interactions, OpenTargets_get_associated_targets_by_disease_efoId, Pharos_get_target

Phase 2: Network Edge Construction

• C-T edges: Bioactivity data (ChEMBL, DrugBank, BindingDB)
• T-D edges: Genetic/functional associations (OpenTargets evidence, GWAS, CTD)
• C-D edges: Clinical trials, CTD chemical-disease, literature co-mentions
• T-T edges: PPI network (STRING, IntAct, OpenTargets interactions, HumanBase)
• Tools: ChEMBL_get_target_activities, OpenTargets_target_disease_evidence, GWAS_search_associations_by_gene, search_clinical_trials, CTD_get_chemical_diseases, STRING_get_interaction_partners, STRING_get_network, intact_search_interactions, humanbase_ppi_analysis

Phase 3: Network Analysis

• Node degree, hub identification, betweenness centrality
• Network modules (drug module vs disease module), module overlap
• Shortest paths between drug targets and disease genes
• Network proximity Z-score calculation
• Functional enrichment (STRING, Enrichr, Reactome)
• Tools: STRING_functional_enrichment, STRING_ppi_enrichment, enrichr_gene_enrichment_analysis, ReactomeAnalysis_pathway_enrichment

Phase 4: Drug Repurposing Predictions

• Identify drugs targeting disease genes (disease-to-compound mode)
• Find diseases associated with drug targets (compound-to-disease mode)
• Rank candidates by composite Network Pharmacology Score
• Predict mechanisms via shared pathways and network paths
• Tools: OpenTargets_get_associated_drugs_by_target_ensemblID, drugbank_get_drug_name_and_description_by_target_name, drugbank_get_pathways_reactions_by_drug_or_id

Phase 5: Polypharmacology Analysis

• Multi-target profiling (primary vs off-targets)
• Disease module coverage calculation
• Target family analysis and selectivity
• Tools: OpenTargets_get_target_classes_by_ensemblID, DGIdb_get_gene_druggability, OpenTargets_get_target_tractability_by_ensemblID

Phase 6: Safety and Toxicity Context

• Adverse event profiling (FAERS disproportionality, OpenTargets AEs)
• Target safety (gene constraints, expression, safety profiles)
• FDA warnings, black box status
• Tools: FAERS_calculate_disproportionality, FAERS_filter_serious_events, FAERS_count_death_related_by_drug, FDA_get_warnings_and_cautions_by_drug_name, OpenTargets_get_drug_adverse_events_by_chemblId, OpenTargets_get_target_safety_profile_by_ensemblID, gnomad_get_gene_constraints

Phase 7: Validation Evidence

• Clinical trials for drug-disease pair
• Literature evidence (PubMed, EuropePMC)
• ADMET predictions if SMILES available
• Pharmacogenomics data
• Tools: search_clinical_trials, clinical_trials_get_details, PubMed_search_articles, EuropePMC_search_articles, ADMETAI_predict_toxicity, PharmGKB_get_drug_details

Phase 8: Report Generation

• Compute Network Pharmacology Score from components
• Generate report using template
• Include completeness checklist

Full step-by-step code examples: ANALYSIS_PROCEDURES.md Report template: REPORT_TEMPLATE.md

Critical Tool Parameter Notes

• DrugBank tools: ALL require query, case_sensitive, exact_match, limit (4 params, ALL required)
• FAERS analytics tools: ALL require operation parameter
• FAERS count tools: Use medicinalproduct NOT drug_name
• OpenTargets tools: Return nested {data: {entity: {field: ...}}} structure
• PubMed_search_articles: Returns plain list of dicts, NOT {articles: [...]}
• ReactomeAnalysis_pathway_enrichment: Takes space-separated identifiers string, NOT array
• ensembl_lookup_gene: REQUIRES species='homo_sapiens' parameter

Full tool parameter reference and response structures: TOOL_REFERENCE.md

Fallback Strategies

Reference Files

Related Skills

• tooluniverse-drug-repurposing - Drug repurposing without network analysis
• tooluniverse-drug-target-validation - Target validation
• tooluniverse-adverse-event-detection - Adverse event detection
• tooluniverse-systems-biology - Systems biology
• tooluniverse-protein-interactions - Protein interactions