tooluniverse-network-pharmacology

name	tooluniverse-network-pharmacology
description	Compound-target-disease network construction and analysis for drug repurposing, polypharmacology discovery, and multi-target drug design. Uses STRING, BioGRID, ChEMBL, DGIdb, OMIM, OpenTargets. Use for off-target effect prediction, network-based drug repurposing, and identifying molecules with desired multi-target profile.
disable-model-invocation	true

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

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.

LOOK UP DON'T GUESS - Retrieve actual target lists, network data, and clinical evidence from tools. Do not infer network relationships from drug class alone.

IMPORTANT: Always use English terms in tool calls, even if the user writes in another language. Respond in the user's language.

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Polypharmacology Reasoning (Start Here)

Before building any network, reason about what kind of multi-target effect you are dealing with:

A drug hitting multiple targets is either polypharmacology (desired multi-target) or promiscuity (undesired off-target). The distinction depends on whether the additional targets contribute to efficacy or cause toxicity.

Use this framework to guide the analysis:

• Desired polypharmacology: multiple targets all lie within the same disease module or pathway. Example: a kinase inhibitor that hits both EGFR and ERBB2 in the same signaling cascade. Look for pathway co-membership and disease module overlap. This is a network proximity argument.
• Off-target promiscuity: additional targets are in unrelated pathways, especially those associated with known toxicity (hERG for cardiotoxicity, CYP3A4 for drug interactions, COX-1 for GI toxicity). Look for these in the safety phase before claiming benefit.
• Repurposing hypothesis: the drug's known targets have strong genetic/functional evidence for the new disease. Network proximity (Z-score) quantifies this. A Z < -2 with p < 0.01 is meaningful signal; a Z near 0 means the targets are essentially unconnected to the disease module.
• Mechanism ambiguity: if a drug has 10+ known targets, do not treat all as therapeutically relevant. Start with primary mechanism-of-action targets, then ask whether secondary targets add to or subtract from the therapeutic window.

Document this reasoning explicitly in the report before listing candidates.

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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
• 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

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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Key Principles

1. Report-first approach - Create report file FIRST, then populate progressively
2. Entity disambiguation FIRST - Resolve all identifiers before analysis
3. Reason about polypharmacology type - Desired vs. promiscuous (see above)
4. Bidirectional network - Construct C-T-D network from both directions
5. Rank candidates - Prioritize by composite Network Pharmacology Score
6. Mechanism prediction - Explain HOW drug could work 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

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Network Pharmacology Score (0-100)

Five components with explicit reasoning at each step:

• Network Proximity (35 pts): Z < -2, p < 0.01 earns full points. A drug whose targets are in a different network neighborhood from the disease module scores near zero here. Do not claim proximity without computing the Z-score.
• Clinical Evidence (25 pts): Approved for related indication earns full points. Clinical trial evidence earns partial credit. Computational prediction alone earns none.
• Target-Disease Association (20 pts): Strong genetic evidence (GWAS, rare variants) for the drug's primary targets in the new disease.
• Safety Profile (10 pts): FDA-approved, favorable safety in target population.
• Mechanism Plausibility (10 pts): A clear pathway mechanism with functional evidence, not just co-mention in literature.

Priority tiers: 80-100 = high repurposing potential (proceed to experimental validation); 60-79 = good potential (needs mechanistic validation); 40-59 = moderate potential (high-risk/high-reward); 0-39 = low potential.

Evidence grades: T1 = human clinical proof; T2 = functional experimental evidence (IC50 < 1 uM, CRISPR screen); T3 = association/computational (GWAS hit, network proximity); T4 = prediction or text-mining only.

Full scoring details: SCORING_REFERENCE.md

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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

• Hub identification: which targets are most connected in the drug-disease subnetwork
• Shortest paths between drug targets and disease genes: how many hops, through which intermediaries
• Network proximity Z-score: are drug targets closer to disease module than random expectation
  • Use the Network_proximity tool — Guney/Barabasi (2016) + Menche (2015) set-distance with a degree-matched Z-score, computed deterministically from a graph you supply (inline edges or an edgelist_path) plus two node sets (set_a/set_b, or the aliases targets/disease_genes). measure = closest (default), shortest, or separation (s_AB < 0 ⇒ overlapping modules). Returns value, z_score, p_value.
  • Feed it the edges from Phase 2. Tested end-to-end: STRING_get_network returns rows with preferredName_A/preferredName_B (gene symbols) — map each to a [A, B] pair and pass as edges; the IDs line up with symbol-based gene sets natively (no conversion).
  • Use a LARGE interactome for the null. A small query-centered subnetwork (e.g. STRING_get_network with a low limit) makes the degree-matched random sets nearly identical to the real ones, giving an uninformative z>0, p≈1. For a meaningful Z, pull the broad interactome (high limit, or a full network via NDEx_get_network), not just the immediate neighborhood. (The skill's scripts/network_proximity.py, which downloads the full STRING network, is the CLI equivalent.)
• Functional enrichment to identify shared biological processes
• Tools: Network_proximity, 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

• Classify each secondary target as contributing to efficacy or representing off-target risk
• Disease module coverage: what fraction of disease genes are hit directly or within 1 hop
• 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, get_clinical_trial_descriptions, PubMed_search_articles, EuropePMC_search_articles, ADMETAI_predict_toxicity, PharmGKB_get_drug_details

Phase 8: Report Generation

• Compute Network Pharmacology Score from components
• Document polypharmacology reasoning (desired vs. promiscuous)
• Generate report using template
• Include completeness checklist

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

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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

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Fallback Strategies

When a tool fails, try the next in chain before reporting "no data":

• Compound ID: OpenTargets drug lookup -> ChEMBL search -> PubChem CID lookup
• Target ID: OpenTargets target lookup -> ensembl_lookup_gene -> MyGene_query_genes
• Disease ID: OpenTargets disease lookup -> ols_search_efo_terms -> CTD_get_chemical_diseases
• Drug targets: OpenTargets drug mechanisms -> DrugBank targets -> DGIdb interactions
• Disease targets: OpenTargets disease targets -> CTD gene-diseases -> GWAS associations
• PPI network: STRING interactions -> OpenTargets interactions -> IntAct interactions
• Pathways: ReactomeAnalysis enrichment -> enrichr enrichment -> STRING functional enrichment
• Clinical trials: search_clinical_trials -> ClinicalTrials_search_studies -> PubMed clinical
• Safety: FAERS + FDA -> OpenTargets AEs -> DrugBank safety
• Literature: PubMed search -> EuropePMC search -> OpenTargets publications

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Reference Files

• ANALYSIS_PROCEDURES.md - Full code examples for each phase
• REPORT_TEMPLATE.md - Markdown template for final report output
• SCORING_REFERENCE.md - Detailed scoring rubric and computation method
• TOOL_REFERENCE.md - Tool signatures, response structures, troubleshooting
• USE_PATTERNS.md - Common analysis patterns and edge case strategies
• QUICK_START.md - Quick-start guide with minimal examples

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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