tooluniverse-network-pharmacology

What this skill does

## 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](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
- Functional enrichment to identify shared biological processes
- 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: `Ope