# Network Pharmacology

> ToolUniverse workflow — Network Pharmacology

- Skill: `lamm-mit/network-pharmacology` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add lamm-mit/network-pharmacology`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lamm-mit/network-pharmacology/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: lamm-mit (https://skillmd.com/u/lamm-mit)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lamm-mit/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. 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

| Parameter | Required | Description | Example |
|-----------|----------|-------------|---------|
| **entity** | Yes | Compound name/ID, target gene symbol/ID, or disease name/ID | `metformin`, `EGFR`, `Alzheimer disease` |
| **entity_type** | No | Type hint: `compound`, `target`, or `disease` (auto-detected if omitted) | `compound` |
| **analysis_mode** | No | `compound-to-disease`, `disease-to-compound`, `target-centric`, `bidirectional` (default) | `bidirectional` |
| **secondary_entity** | No | Second entity for focused analysis (e.g., disease for compound input) | `Alzheimer disease` |

---

## Network Pharmacology Score (0-100)

### Score Components

**Network Proximity** (0-35 points):
- Strong proximity (Z < -2, p < 0.01): 35 points
- Moderate proximity (Z < -1, p < 0.05): 20 points
- Weak proximity (Z < -0.5): 10 points
- No proximity: 0 points

**Clinical Evidence** (0-25 points):
- Approved for related indication: 25 points
- Active clinical trials: 15 points
- Completed trials with positive results: 10 points
- Preclinical only: 5 points

**Target-Disease Association** (0-20 points):
- Strong genetic evidence (GWAS, rare variants): 20 points
- Moderate evidence (pathways, literature): 12 points
- Weak evidence (computational only): 5 points

**Safety Profile** (0-10 points):
- FDA-approved, favorable safety: 10 points
- Known manageable adverse events: 7 points
- Significant safety concerns: 3 points
- Black box warning relevant to indication: 0 points

**Mechanism Plausibility** (0-10 points):
- Clear pathway mechanism with functional evidence: 10 points
- Indirect mechanism via network neighbors: 6 points
- Purely computational prediction: 2 points

### Priority Tiers

| Score | Tier | Recommendation |
|-------|------|----------------|
| **80-100** | Tier 1 | High repurposing potential - proceed with experimental validation |
| **60-79** | Tier 2 | Good potential - needs mechanistic validation |
| **40-59** | Tier 3 | Moderate potential - high-risk/high-reward, needs extensive validation |
| **0-39** | Tier 4 | Low potential - consider alternative approaches |

### Evidence Grading System

| Tier | Symbol | Criteria | Examples |
|------|--------|----------|----------|
| **T1** | [T1] | Human clinical proof, regulatory evidence | FDA-approved indication, Phase III trial, patient genomics |
| **T2** | [T2] | Functional experimental evidence | Bioactivity data (IC50 < 1 uM), CRISPR screen, animal model |
| **T3** | [T3] | Association/computational evidence | GWAS hit, network proximity, pathway enrichment, expression |
| **T4** | [T4] | Prediction, annotation, text-mining | AlphaFold prediction, database annotation, literature co-mention |

---

## 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

---

## Complete Workflow

### Phase 0: Entity Disambiguation and Report Setup

**Step 0.1**: Create the report file immediately.

```python
# Create report file FIRST
report_path = "[entity]_network_pharmacology_report.md"
# Write header and placeholder sections
```

**Step 0.2**: Resolve the input entity to all required identifiers.

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

# === COMPOUND DISAMBIGUATION ===
# Get ChEMBL ID from drug name
drug_info = tu.tools.OpenTargets_get_drug_chembId_by_generic_name(
    drugName="metformin"
)
# Returns: {data: {search: {hits: [{id: "CHEMBL1431", name: "METFORMIN", ...}]}}}
chembl_id = drug_info['data']['search']['hits'][0]['id']

# Get drug details (mechanism, indications)
drug_desc = tu.tools.OpenTargets_get_drug_id_description_by_name(
    drugName="metformin"
)

# Get DrugBank info
drugbank_info = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id(
    query="metformin", case_sensitive=False, exact_match=True, limit=1
)
# Returns: {status: "success", data: {drug_name: ..., drugbank_id: ..., ...}}

# Get PubChem CID and SMILES
pubchem_cid = tu.tools.PubChem_get_CID_by_compound_name(
    name="metformin"
)
# Returns: {IdentifierList: {CID: [4091]}}
cid = pubchem_cid['IdentifierList']['CID'][0]

# Get SMILES
pubchem_props = tu.tools.PubChem_get_compound_properties_by_CID(
    cid=cid
)
# Returns: {CID: ..., MolecularWeight: ..., ConnectivitySMILES: ..., IUPACName: ...}

# === TARGET DISAMBIGUATION ===
# Get Ensembl ID from gene symbol
target_info = tu.tools.OpenTargets_get_target_id_description_by_name(
    targetName="PSEN1"
)
# Returns: {data: {search: {hits: [{id: "ENSG00000080815", name: "PSEN1", ...}]}}}
ensembl_id = target_info['data']['search']['hits'][0]['id']

# Get gene details from Ensembl
gene_details = tu.tools.ensembl_lookup_gene(
    gene_id=ensembl_id, species='homo_sapiens'
)
# Returns: {status: "success", data: {display_name: ..., biotype: ..., ...}}

# Get MyGene info for cross-references
mygene = tu.tools.MyGene_query_genes(query="PSEN1")

# === DISEASE DISAMBIGUATION ===
# Get disease ID and description
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(
    diseaseName="Alzheimer disease"
)
# Returns: {data: {search: {hits: [{id: "MONDO_0004975", name: "Alzheimer disease", ...}]}}}
disease_id = disease_info['data']['search']['hits'][0]['id']

# Get disease description
disease_desc = tu.tools.OpenTargets_get_disease_description_by_efoId(
    efoId=disease_id
)

# Get cross-references
disease_ids = tu.tools.OpenTargets_get_disease_ids_by_efoId(efoId=disease_id)
```

---

### Phase 1: Network Node Identification

**Step 1.1**: Identify compound nodes.

```python
# Get drug targets and mechanism of action from OpenTargets
drug_moa = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(
    chemblId=chembl_id
)
# Returns: {data: {drug: {mechanismsOfAction: {rows: [{mechanismOfAction: ..., actionType: ..., targetName: ..., targets: [{id, approvedSymbol}]}]}}}}

# Get associated targets from OpenTargets
drug_targets_ot = tu.tools.OpenTargets_get_associated_targets_by_drug_chemblId(
    chemblId=chembl_id, size=50
)
# Returns: {data: {drug: {linkedTargets: {count: N, rows: [{id, approvedSymbol}]}}}}

# Get targets from DrugBank
drug_targets_db = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id(
    query="metformin", case_sensitive=False, exact_match=True, limit=1
)
# Returns: {status: "success", data: {drug_name: ..., targets: [{id, name, organism, actions}]}}

# Get drug-gene interactions from DGIdb
dgidb_interactions = tu.tools.DGIdb_get_drug_gene_interactions(
    genes=["PSEN1", "APP", "BACE1"]  # for disease-to-compound mode
)
# Returns: {data: {genes: {nodes: [{name, interactions: [{drug: {name, conceptId}, interactionTypes: [{type}]}]}]}}}

# Get chemical-gene interactions from CTD
ctd_genes = tu.tools.CTD_get_chemical_gene_interactions(
    input_terms="Metformin"
)
# Returns: {data: [{ChemicalName, GeneSymbol, InteractionActions, ...}]}

# Get STITCH chemical-protein interactions
stitch_id = tu.tools.STITCH_resolve_identifier(
    identifier="metformin", species=9606
)
# Then query interactions
stitch_interactions = tu.tools.STITCH_get_chemical_protein_interactions(
    identifiers=["CIDm000004091"], species=9606
)

# Get current indications
drug_indications = tu.tools.OpenTargets_get_drug_indications_by_chemblId(
    chemblId=chembl_id, size=50
)
# Returns: {data: {drug: {indications: {rows: [{disease: {id, name}, maxPhaseForIndication, references}]}}}}

# Check FDA approval status
fda_approval = tu.tools.OpenTargets_get_drug_approval_status_by_chemblId(
    chemblId=chembl_id
)

# Get associated diseases for drug (all trials/investigations)
drug_diseases = tu.tools.OpenTargets_get_associated_diseases_by_drug_chemblId(
    chemblId=chembl_id, size=50
)
# Returns: {data: {drug: {linkedDiseases: {count: N, rows: [{id, name, description}]}}}}
```

**Step 1.2**: Identify target nodes (disease-associated targets).

```python
# Get disease-associated targets from OpenTargets
disease_targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(
    efoId=disease_id, limit=50
)
# Returns: {data: {disease: {associatedTargets: {count: N, rows: [{target: {id, approvedSymbol}, score}]}}}}

# Get disease-target evidence for top targets
for target in disease_targets['data']['disease']['associatedTargets']['rows'][:10]:
    evidence = tu.tools.OpenTargets_target_disease_evidence(
        efoId=disease_id,
        ensemblId=target['target']['id']
    )

# Get GWAS evidence for targets
gwas_studies = tu.tools.OpenTargets_search_gwas_studies_by_disease(
    diseaseIds=[disease_id], size=20
)
# Returns: {data: {studies: {count: N, rows: [...]}}}

# Get gene-disease associations from CTD
ctd_diseases = tu.tools.CTD_get_gene_diseases(
    input_terms="PSEN1"
)

# Get Pharos target info (druggability levels)
for gene in ["PSEN1", "APP", "BACE1"]:
    pharos = tu.tools.Pharos_get_target(target_name=gene)
    # Returns target development level (Tclin, Tchem, Tbio, Tdark)
```

**Step 1.3**: Identify disease nodes and related conditions.

```python
# Get related/similar diseases
related_diseases = tu.tools.OpenTargets_get_similar_entities_by_disease_efoId(
    efoId=disease_id, size=10, threshold=0.5
)
# Returns: {data: {disease: {similarEntities: [{id, category, score, object: {id, name}}]}}}

# Get disease hierarchy (children/parents)
disease_children = tu.tools.OpenTargets_get_disease_descendants_children_by_efoId(
    efoId=disease_id
)
disease_parents = tu.tools.OpenTargets_get_disease_ancestors_parents_by_efoId(
    efoId=disease_id
)

# Get phenotypes associated with disease
disease_phenotypes = tu.tools.OpenTargets_get_associated_phenotypes_by_disease_efoId(
    efoId=disease_id, size=20
)

# Get therapeutic areas
disease_areas = tu.tools.OpenTargets_get_disease_therapeutic_areas_by_efoId(
    efoId=disease_id
)
```

---

### Phase 2: Network Edge Construction

**Step 2.1**: Compound-target edges (bioactivity data).

```python
# Get ChEMBL bioactivity data for drug targets
chembl_activities = tu.tools.ChEMBL_get_target_activities(
    target_chembl_id__exact="CHEMBL2111455",  # example target ChEMBL ID
    limit=50
)
# Returns activity data with pchembl_value, standard_type (IC50, Ki, etc.)

# Search ChEMBL mechanisms (all mechanisms for drug)
all_mechanisms = tu.tools.ChEMBL_search_mechanisms(
    query="metformin", limit=50
)

# Get DrugBank drug targets with action types
db_targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id(
    query="metformin", case_sensitive=False, exact_match=True, limit=1
)
# Returns: targets with action type (inhibitor, substrate, etc.)

# Get pharmacology from DrugBank
db_pharmacology = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id(
    query="metformin", case_sensitive=False, exact_match=True, limit=1
)

# Get BindingDB ligands for key targets (if UniProt ID available)
# binding_data = tu.tools.BindingDB_get_ligands_by_uniprot(uniprot_accession="P49768")
```

**Step 2.2**: Target-disease edges (genetic and functional associations).

```python
# Get OpenTargets target-disease evidence
for target in top_disease_targets[:10]:
    td_evidence = tu.tools.OpenTargets_target_disease_evidence(
        efoId=disease_id,
        ensemblId=target['target']['id']
    )
    # Returns: evidence across datasources (genetics, pathways, literature, etc.)

# Get GWAS associations for key genes
for gene_symbol in ["PSEN1", "APP", "APOE"]:
    gwas_assoc = tu.tools.GWAS_search_associations_by_gene(gene_name=gene_symbol)

# Get gene-disease links from CTD
ctd_gene_diseases = tu.tools.CTD_get_gene_diseases(
    input_terms="PSEN1"
)

# Get PharmGKB gene details (pharmacogenomics)
pharmgkb_gene = tu.tools.PharmGKB_get_gene_details(gene_symbol="PSEN1")
```

**Step 2.3**: Compound-disease edges (clinical evidence).

```python
# Get clinical trial evidence
trials = tu.tools.search_clinical_trials(
    query_term="metformin",
    condition="Alzheimer",
    pageSize=20
)
# Returns: {studies: [{NCT ID, brief_title, brief_summary, ...}]}

# Also search with clinical_trials_search
trials2 = tu.tools.clinical_trials_search(
    query="metformin Alzheimer disease",
    limit=20
)

# Get CTD chemical-disease associations
ctd_chem_diseases = tu.tools.CTD_get_chemical_diseases(
    input_terms="Metformin"
)
# Returns: [{ChemicalName, DiseaseName, DirectEvidence: "therapeutic"|"marker/mechanism", ...}]

# Literature evidence (PubMed co-mentions)
pubmed_results = tu.tools.PubMed_search_articles(
    query="metformin Alzheimer disease",
    max_results=50
)
# Returns: list of {pmid, title, authors, journal, pub_date, ...}

# Europe PMC search for broader coverage
europepmc_results = tu.tools.EuropePMC_search_articles(
    query="metformin Alzheimer disease",
    limit=50
)
```

**Step 2.4**: Target-target edges (PPI network).

```python
# Get STRING protein-protein interactions
string_ppi = tu.tools.STRING_get_interaction_partners(
    protein_ids=["PSEN1", "APP", "APOE", "BACE1", "MAPT"],
    species=9606,
    limit=20
)
# Returns: {status: "success", data: [{stringId_A, stringId_B, preferredName_A, preferredName_B, score, ...}]}

# Get full STRING network
string_network = tu.tools.STRING_get_network(
    protein_ids=["PSEN1", "APP", "APOE", "BACE1", "MAPT"],
    species=9606
)

# Get IntAct interactions
intact_results = tu.tools.intact_search_interactions(
    query="PSEN1", max=20
)

# Get OpenTargets target interactions
ot_interactions = tu.tools.OpenTargets_get_target_interactions_by_ensemblID(
    ensemblId="ENSG00000080815",  # PSEN1
    size=20
)
# Returns: {data: {target: {interactions: {count: N, rows: [{intA, targetA: {id, approvedSymbol}, intB, targetB: {id, approvedSymbol}, score, sourceDatabase}]}}}}

# HumanBase tissue-specific PPI
humanbase_ppi = tu.tools.humanbase_ppi_analysis(
    gene_list=["PSEN1", "APP", "APOE", "BACE1", "MAPT"],
    tissue="brain",
    max_node=50,
    interaction="sn",
    string_mode="physical"
)
```

---

### Phase 3: Network Analysis

**Step 3.1**: Network topology analysis (computed from collected data).

```
Compute from Phase 2 data:

1. Node Degree:
   - Count connections per node from STRING + IntAct + OpenTargets interactions
   - Drug targets: connections from bioactivity data
   - Disease genes: connections from PPI data

2. Hub Identification:
   - Nodes with degree > mean + 2*SD are hubs
   - Hub genes in disease module = priority therapeutic targets

3. Betweenness Centrality:
   - Nodes on shortest paths between drug targets and disease genes
   - High betweenness = potential mediating/bridging targets

4. Network Modules:
   - Disease module: cluster of disease-associated genes in PPI
   - Drug module: cluster of drug target genes in PPI
   - Module overlap = direct network relevance

5. Shortest Paths:
   - Paths from each drug target to each disease gene via PPI
   - Shortest path length < 2 = direct interaction
   - Path length 2-3 = close proximity
   - Path length > 4 = distant, weaker association
```

**Step 3.2**: Network proximity calculation.

```
Network Proximity Z-score (computed from data):

1. Collect drug target set T_d from Phase 1
2. Collect disease gene set G_d from Phase 1
3. For each drug target t in T_d and disease gene g in G_d:
   - Find shortest path d(t,g) in PPI network from Phase 2
4. Compute closest proximity: d_c = mean of min distances
5. Compare against random expectation:
   - Sample N random gene sets of same size as T_d
   - Compute proximity for each random set
   - Z = (d_c - mean_random) / sd_random
6. Z < -2: strong proximity (35 points)
   Z < -1: moderate proximity (20 points)
   Z < -0.5: weak proximity (10 points)
   Z >= -0.5: no proximity (0 points)

Practical computation from STRING/OpenTargets PPI data:
- Count direct interactions between drug targets and disease genes
- Count shared PPI partners (second-degree connections)
- Calculate overlap coefficient = shared_partners / min(degree_t, degree_d)
- Use number of shared pathways as additional proximity metric
```

**Step 3.3**: Functional enrichment analysis.

```python
# STRING functional enrichment for disease genes
disease_gene_symbols = [t['target']['approvedSymbol']
                        for t in disease_targets['data']['disease']['associatedTargets']['rows'][:20]]

string_enrichment = tu.tools.STRING_functional_enrichment(
    protein_ids=disease_gene_symbols,
    species=9606
)

# STRING PPI enrichment (statistical test for network connectivity)
string_ppi_enrich = tu.tools.STRING_ppi_enrichment(
    protein_ids=disease_gene_symbols,
    species=9606
)

# Enrichr pathway analysis
enrichr_results = tu.tools.enrichr_gene_enrichment_analysis(
    gene_list=disease_gene_symbols,
    libs=["KEGG_2021_Human", "Reactome_2022", "GO_Biological_Process_2023"]
)
# Returns enrichment results per library

# Reactome pathway enrichment
reactome_enrichment = tu.tools.ReactomeAnalysis_pathway_enrichment(
    identifiers=" ".join(disease_gene_symbols)
)
# Returns: {data: {pathways: [{pathway_id, name, p_value, fdr, entities_found, ...}]}}
```

---

### Phase 4: Drug Repurposing Predictions

**Step 4.1**: Identify and rank repurposing candidates.

```python
# For disease-to-compound mode: Find drugs targeting disease genes
repurposing_candidates = []

for target in disease_targets['data']['disease']['associatedTargets']['rows'][:20]:
    gene_symbol = target['target']['approvedSymbol']
    ensembl_id = target['target']['id']
    target_score = target['score']

    # Get drugs from OpenTargets
    target_drugs = tu.tools.OpenTargets_get_associated_drugs_by_target_ensemblID(
        ensemblId=ensembl_id, size=20
    )

    # Get drugs from DGIdb
    dgidb_drugs = tu.tools.DGIdb_get_drug_gene_interactions(genes=[gene_symbol])

    # Get drugs from DrugBank
    drugbank_drugs = tu.tools.drugbank_get_drug_name_and_description_by_target_name(
        query=gene_symbol, case_sensitive=False, exact_match=False, limit=20
    )

    # Collect and deduplicate candidates
    # Score each by: target_disease_score * drug_target_affinity * approval_status

# For compound-to-disease mode: Already have drug targets, find their diseases
for target in drug_targets:
    # Get diseases associated with each drug target
    target_diseases = tu.tools.OpenTargets_get_diseases_phenotypes_by_target_ensembl(
        ensemblId=target['id'], size=20
    )
```

**Step 4.2**: Mechanism prediction for repurposing candidates.

```python
# For each repurposing candidate, trace the network path:
# Drug -> Direct targets -> PPI neighbors -> Disease genes

# Get drug mechanism
drug_moa = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(
    chemblId=candidate_chembl_id
)

# Get pathways shared between drug targets and disease genes
drug_target_genes = [t['approvedSymbol'] for t in drug_moa_targets]
combined_genes = list(set(drug_target_genes + disease_gene_symbols[:10]))

# Pathway enrichment for combined gene set
combined_pathways = tu.tools.ReactomeAnalysis_pathway_enrichment(
    identifiers=" ".join(combined_genes)
)

# Check for specific pathway overlap
drug_pathways = tu.tools.drugbank_get_pathways_reactions_by_drug_or_id(
    query=drug_name, case_sensitive=False, exact_match=True, limit=1
)
```

---

### Phase 5: Polypharmacology Analysis

**Step 5.1**: Multi-target profiling.

```python
# Get ALL targets of compound (on-targets + off-targets)
# From OpenTargets
all_drug_targets = tu.tools.OpenTargets_get_associated_targets_by_drug_chemblId(
    chemblId=chembl_id, size=100
)

# From DrugBank (includes enzymes, carriers, transporters)
db_full_targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id(
    query=drug_name, case_sensitive=False, exact_match=True, limit=1
)

# From CTD (chemical-gene interactions, includes indirect)
ctd_interactions = tu.tools.CTD_get_chemical_gene_interactions(
    input_terms=drug_name
)

# Classify targets: primary (mechanism) vs secondary (off-target)
# Count disease module coverage
drug_target_set = set(drug_target_genes)
disease_gene_set = set(disease_gene_symbols[:50])
overlap = drug_target_set & disease_gene_set
coverage = len(overlap) / len(disease_gene_set) if disease_gene_set else 0

# Target family analysis
for gene in drug_target_genes[:10]:
    target_class = tu.tools.OpenTargets_get_target_classes_by_ensemblID(
        ensemblId=gene_ensembl_id
    )
```

**Step 5.2**: Selectivity analysis.

```python
# Get target druggability and development levels
for gene in drug_target_genes[:10]:
    # DGIdb druggability
    druggability = tu.tools.DGIdb_get_gene_druggability(genes=[gene])

    # Pharos target development level
    pharos_info = tu.tools.Pharos_get_target(target_name=gene)
    # Tclin = known drug targets, Tchem = has chemical tools, Tbio = has biology, Tdark = dark target

    # OpenTargets tractability
    tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(
        ensemblId=gene_ensembl_id
    )
```

---

### Phase 6: Safety and Toxicity Context

**Step 6.1**: Adverse event profiling.

```python
# Get FAERS adverse event data
faers_ae = tu.tools.FAERS_search_reports_by_drug_and_reaction(
    drug_name=drug_name, limit=100
)

# Get serious events
faers_serious = tu.tools.FAERS_filter_serious_events(
    operation="filter_serious_events",
    drug_name=drug_name,
    seriousness_type="all"
)

# Get death reports
faers_death = tu.tools.FAERS_count_death_related_by_drug(
    medicinalproduct=drug_name
)
# Returns: [{term: "alive", count: N}, {term: "death", count: N}]

# Calculate disproportionality for key AEs
faers_signal = tu.tools.FAERS_calculate_disproportionality(
    operation="calculate_disproportionality",
    drug_name=drug_name,
    adverse_event="lactic acidosis"  # example
)
# Returns: {metrics: {PRR: {value, ci_95_lower, ci_95_upper}, ROR: {...}}, signal_detection: {signal_detected, signal_strength}}

# Get FDA warnings
fda_warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name(
    drug_name=drug_name
)

# Get black box warning status
bbox_warning = tu.tools.OpenTargets_get_drug_blackbox_status_by_chembl_ID(
    chemblId=chembl_id
)

# Get drug adverse events from OpenTargets
ot_ae = tu.tools.OpenTargets_get_drug_adverse_events_by_chemblId(
    chemblId=chembl_id
)
# Returns: {data: {drug: {adverseEvents: {count, rows: [{name, meddraCode, count, logLR}]}}}}

# Get drug warnings from OpenTargets
drug_warnings = tu.tools.OpenTargets_get_drug_warnings_by_chemblId(
    chemblId=chembl_id
)
```

**Step 6.2**: Target safety profiling.

```python
# For each drug target, assess safety
for target_ensembl_id in drug_target_ensembl_ids[:10]:
    # OpenTargets target safety profile
    safety = tu.tools.OpenTargets_get_target_safety_profile_by_ensemblID(
        ensemblId=target_ensembl_id
    )

    # Gene constraint (is target essential?)
    constraints = tu.tools.gnomad_get_gene_constraints(gene_symbol=gene_symbol)
    # High pLI (>0.9) = loss-of-function intolerant = essential gene = safety concern

    # Expression pattern (broadly expressed = more off-target risk)
    expression = tu.tools.HPA_get_rna_expression_by_source(
        gene_name=gene_symbol,
        source_type="tissue",
        source_name="brain"
    )
```

---

### Phase 7: Validation Evidence

**Step 7.1**: Clinical precedent.

```python
# Search clinical trials for drug + disease combination
trials = tu.tools.search_clinical_trials(
    query_term=drug_name,
    condition=disease_name,
    pageSize=20
)

# Get trial details for each match
for trial in trials.get('studies', [])[:5]:
    nct_id = trial['NCT ID']
    trial_details = tu.tools.clinical_trials_get_details(nct_id=nct_id)
    trial_outcomes = tu.tools.extract_clinical_trial_outcomes(nct_id=nct_id)
    trial_ae = tu.tools.extract_clinical_trial_adverse_events(nct_id=nct_id)

# Check approved indications
approved = tu.tools.OpenTargets_get_approved_indications_by_drug_chemblId(
    chemblId=chembl_id
)
# Returns: {data: {drug: {approvedIndications: ["EFO_XXXXX", ...]}}}
```

**Step 7.2**: Literature evidence.

```python
# PubMed search for drug-disease co-mentions
pubmed_evidence = tu.tools.PubMed_search_articles(
    query=f"{drug_name} {disease_name} repurposing OR repositioning OR network pharmacology",
    max_results=50
)
# Returns: list of {pmid, title, authors, journal, pub_date, ...}

# Europe PMC with broader scope
europepmc_evidence = tu.tools.EuropePMC_search_articles(
    query=f"{drug_name} {disease_name}",
    limit=50
)

# OpenTargets publications for drug
ot_drug_pubs = tu.tools.OpenTargets_get_publications_by_drug_chemblId(
    chemblId=chembl_id, size=20
)

# OpenTargets publications for disease
ot_disease_pubs = tu.tools.OpenTargets_get_publications_by_disease_efoId(
    efoId=disease_id, size=20
)

# Get guideline searches
guidelines = tu.tools.PubMed_Guidelines_Search(query=f"{drug_name} {disease_name}")
```

**Step 7.3**: Experimental evidence.

```python
# ChEMBL bioactivity data
chembl_bioactivity = tu.tools.ChEMBL_search_drugs(
    query=drug_name, limit=10
)

# Check ADMET predictions (for novel formulation contexts)
if smiles:
    admet = tu.tools.ADMETAI_predict_toxicity(smiles=[smiles])
    bbb = tu.tools.ADMETAI_predict_BBB_penetrance(smiles=[smiles])
    bioavail = tu.tools.ADMETAI_predict_bioavailability(smiles=[smiles])

# PharmGKB pharmacogenomics data
pharmgkb_drug = tu.tools.PharmGKB_get_drug_details(drug_name=drug_name)
pharmgkb_clin = tu.tools.PharmGKB_get_clinical_annotations(query=drug_name)
```

---

### Phase 8: Report Generation

**Step 8.1**: Compute Network Pharmacology Score.

```
Score Calculation:

1. Network Proximity Score (0-35):
   - Count direct drug target <-> disease gene interactions in PPI
   - Count shared PPI partners
   - Count shared pathways
   - Map to Z-score equivalent based on overlap significance

2. Clinical Evidence Score (0-25):
   - Search clinical trials for drug-disease pair
   - Check approved indications for related diseases
   - Check max clinical trial phase

3. Target-Disease Association Score (0-20):
   - Average OpenTargets association score for drug targets in disease
   - Weight by evidence type (genetic > functional > computational)

4. Safety Score (0-10):
   - FDA approval status (+5)
   - Black box warning (-3)
   - Death reports proportion
   - Off-target count penalty

5. Mechanism Plausibility Score (0-10):
   - Known mechanism for related indication (+5)
   - Pathway evidence (+3)
   - Network path length to disease module (+2)

Total: sum of components (0-100)
```

**Step 8.2**: Generate comprehensive report.

```markdown
# Network Pharmacology Analysis: [Entity]

## Executive Summary
[2-3 sentence summary of key findings]

## Network Pharmacology Score: [X]/100 - [Tier]
| Component | Score | Max | Evidence |
|-----------|-------|-----|----------|
| Network Proximity | X | 35 | [summary] |
| Clinical Evidence | X | 25 | [summary] |
| Target-Disease Association | X | 20 | [summary] |
| Safety Profile | X | 10 | [summary] |
| Mechanism Plausibility | X | 10 | [summary] |
| **TOTAL** | **X** | **100** | |

## 1. Entity Profile
### Compound: [Name]
- ChEMBL ID: [ID]
- DrugBank ID: [ID]
- SMILES: [SMILES]
- Mechanism: [MOA]
- Approval status: [status]
- Current indications: [list]

### Disease: [Name]
- MONDO/EFO ID: [ID]
- Description: [brief]
- Top associated targets: [list with scores]
- Related diseases: [list]

## 2. Network Topology Summary
- **Total nodes**: X (Y compounds, Z targets, W diseases)
- **Total edges**: X (Y C-T, Z T-D, W C-D, V T-T)
- **Network density**: X
- **Hub nodes**: [list of top hub genes]
- **Modules detected**: X

### Drug Target Module
[List drug targets with degree and betweenness]

### Disease Gene Module
[List disease genes with degree and betweenness]

### Module Overlap
[Shared genes, shared pathways, overlap coefficient]

## 3. Network Proximity
- **Proximity measure**: [metric used]
- **Z-score**: [value]
- **Direct interactions**: X drug target-disease gene pairs
- **Shared PPI partners**: X genes
- **Shared pathways**: X pathways
- **Interpretation**: [strong/moderate/weak proximity]

## 4. Top Repurposing Candidates (Ranked)

### Candidate 1: [Drug Name] - Score: X/100
**ChEMBL ID**: [ID] | **Status**: [Approved/Clinical/Preclinical]
**Current indications**: [list]
**Network path**: Drug -> [target1, target2] -> [PPI] -> [disease gene1, gene2]
**Mechanism prediction**: [how drug could work for disease]
**Clinical evidence**: [trials, literature]
**Safety**: [key concerns]
**Evidence grade**: [T1-T4]

[Repeat for top 10 candidates]

## 5. Polypharmacology Profile
### Target Coverage
- Total drug targets: X
- Disease module targets hit: Y (Z%)
- Primary targets: [list with actions]
- Off-targets: [list with potential effects]

### Multi-Target Effects
[Analysis of synergistic vs antagonistic target modulation]

### Disease Module Coverage
[How well drug targets cover the disease network]

## 6. Pathway Analysis
### Drug-Affected Pathways
[Ranked list of pathways affected by drug]

### Disease-Associated Pathways
[Ranked list of pathways associated with disease]

### Overlapping Pathways (Mechanism)
[Pathways shared between drug and disease - these explain the mechanism]

## 7. Safety Considerations
### Adverse Events
[Top AEs with PRR/ROR where available]

### Target Safety Flags
[Targets with known safety liabilities]

### Off-Target Risks
[Off-targets in critical tissues]

### Drug-Drug Interaction Context
[Key DDI considerations]

## 8. Clinical Precedent
### Clinical Trials
[List of relevant trials with NCT IDs and status]

### Literature Evidence
[Key publications supporting or refuting repurposing hypothesis]
- N papers found for [drug] + [disease]
- Key findings: [summary]

### Pharmacogenomics
[Relevant PGx data]

## 9. Evidence Summary Table
| Finding | Source | Evidence Grade | Confidence |
|---------|--------|---------------|------------|
| [finding1] | [tool/database] | [T1-T4] | [High/Medium/Low] |
| ... | ... | ... | ... |

## 10. Recommendations
### Immediate Actions
1. [Action 1 - e.g., review clinical trial NCT00620191]
2. [Action 2 - e.g., validate mechanism in cell model]

### Further Investigation
1. [Investigation 1]
2. [Investigation 2]

### Risk Mitigation
1. [Risk 1 and mitigation strategy]

## Completeness Checklist
| Phase | Status | Tools Used | Key Findings |
|-------|--------|------------|--------------|
| Entity Disambiguation | Done/Partial/Failed | [tools] | [summary] |
| Compound Node ID | Done/Partial/Failed | [tools] | [summary] |
| Target Node ID | Done/Partial/Failed | [tools] | [summary] |
| Disease Node ID | Done/Partial/Failed | [tools] | [summary] |
| C-T Edges | Done/Partial/Failed | [tools] | [summary] |
| T-D Edges | Done/Partial/Failed | [tools] | [summary] |
| C-D Edges | Done/Partial/Failed | [tools] | [summary] |
| T-T Edges (PPI) | Done/Partial/Failed | [tools] | [summary] |
| Network Topology | Done/Partial/Failed | [computed] | [summary] |
| Network Proximity | Done/Partial/Failed | [computed] | [summary] |
| Pathway Enrichment | Done/Partial/Failed | [tools] | [summary] |
| Repurposing Candidates | Done/Partial/Failed | [tools] | [summary] |
| Mechanism Prediction | Done/Partial/Failed | [analysis] | [summary] |
| Polypharmacology | Done/Partial/Failed | [tools] | [summary] |
| Safety/Toxicity | Done/Partial/Failed | [tools] | [summary] |
| Clinical Precedent | Done/Partial/Failed | [tools] | [summary] |
| Literature Evidence | Done/Partial/Failed | [tools] | [summary] |
| Report Generation | Done/Partial/Failed | - | [summary] |
```

---

## Tool Parameter Reference (Verified)

### Compound Identification
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `OpenTargets_get_drug_chembId_by_generic_name` | `drugName: str` | `{data: {search: {hits: [{id, name, description}]}}}` |
| `OpenTargets_get_drug_id_description_by_name` | `drugName: str` | `{data: {search: {hits: [{id, name, description}]}}}` |
| `drugbank_get_drug_basic_info_by_drug_name_or_id` | `query: str`, `case_sensitive: bool`, `exact_match: bool`, `limit: int` (ALL required) | `{status, data: {drug_name, drugbank_id, ...}}` |
| `PubChem_get_CID_by_compound_name` | `name: str` | `{IdentifierList: {CID: [int]}}` |
| `PubChem_get_compound_properties_by_CID` | `cid: int` | `{CID, MolecularWeight, ConnectivitySMILES, IUPACName}` |
| `ChEMBL_search_drugs` | `query: str`, `limit: int` | `{status, data: {drugs: [...]}}` |

### Target Identification
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `OpenTargets_get_target_id_description_by_name` | `targetName: str` | `{data: {search: {hits: [{id, name, description}]}}}` |
| `ensembl_lookup_gene` | `gene_id: str`, `species: str` (REQUIRED, e.g., "homo_sapiens") | `{status, data: {display_name, biotype, ...}}` |
| `MyGene_query_genes` | `query: str` | Gene info with cross-references |
| `Pharos_get_target` | `target_name: str` | Target with development level |

### Disease Identification
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `OpenTargets_get_disease_id_description_by_name` | `diseaseName: str` | `{data: {search: {hits: [{id, name, description}]}}}` |
| `OpenTargets_get_disease_description_by_efoId` | `efoId: str` | `{data: {disease: {id, name, description}}}` |
| `OpenTargets_get_disease_ids_by_efoId` | `efoId: str` | Disease cross-references |

### Network Edges
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `STRING_get_interaction_partners` | `protein_ids: list[str]`, `species: int` (9606), `limit: int` | `{status, data: [{stringId_A, stringId_B, preferredName_A, preferredName_B, score}]}` |
| `STRING_get_network` | `protein_ids: list[str]`, `species: int` | Network data |
| `STRING_functional_enrichment` | `protein_ids: list[str]`, `species: int` | Enrichment results |
| `STRING_ppi_enrichment` | `protein_ids: list[str]`, `species: int` | PPI enrichment statistics |
| `OpenTargets_get_target_interactions_by_ensemblID` | `ensemblId: str`, `size: int` | `{data: {target: {interactions: {count, rows: [{intA, targetA, intB, targetB, score}]}}}}` |
| `intact_search_interactions` | `query: str`, `max: int` | Interaction data |
| `humanbase_ppi_analysis` | `gene_list: list`, `tissue: str`, `max_node: int`, `interaction: str`, `string_mode: str` (ALL required) | Tissue-specific PPI |

### Drug-Target Edges
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `OpenTargets_get_drug_mechanisms_of_action_by_chemblId` | `chemblId: str` | `{data: {drug: {mechanismsOfAction: {rows: [{mechanismOfAction, actionType, targets}]}}}}` |
| `OpenTargets_get_associated_targets_by_drug_chemblId` | `chemblId: str`, `size: int` | `{data: {drug: {linkedTargets: {count, rows}}}}` |
| `drugbank_get_targets_by_drug_name_or_drugbank_id` | `query, case_sensitive, exact_match, limit` (ALL required) | `{status, data: {targets: [{id, name, organism, actions}]}}` |
| `DGIdb_get_drug_gene_interactions` | `genes: list[str]` | `{data: {genes: {nodes: [{name, interactions}]}}}` |
| `CTD_get_chemical_gene_interactions` | `input_terms: str` | `{data: [{ChemicalName, GeneSymbol, InteractionActions}]}` |
| `ChEMBL_get_target_activities` | `target_chembl_id__exact: str` | Activity data with pchembl_value |

### Target-Disease Edges
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `OpenTargets_get_associated_targets_by_disease_efoId` | `efoId: str`, `limit: int` | `{data: {disease: {associatedTargets: {count, rows: [{target: {id, approvedSymbol}, score}]}}}}` |
| `OpenTargets_target_disease_evidence` | `efoId: str`, `ensemblId: str` (BOTH required) | Evidence data across datasources |
| `CTD_get_gene_diseases` | `input_terms: str` | `{data: [{GeneName, DiseaseName, DirectEvidence}]}` |
| `GWAS_search_associations_by_gene` | `gene_name: str` | GWAS association data |

### Drug-Disease Edges
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `OpenTargets_get_drug_indications_by_chemblId` | `chemblId: str`, `size: int` | `{data: {drug: {indications: {rows: [{disease, maxPhaseForIndication}]}}}}` |
| `OpenTargets_get_associated_diseases_by_drug_chemblId` | `chemblId: str`, `size: int` | `{data: {drug: {linkedDiseases: {count, rows}}}}` |
| `CTD_get_chemical_diseases` | `input_terms: str` | `{data: [{ChemicalName, DiseaseName, DirectEvidence}]}` |
| `search_clinical_trials` | `query_term: str` (REQUIRED), `condition: str`, `pageSize: int` | `{studies: [{NCT ID, brief_title, ...}]}` |

### Pathway Analysis
| Tool | Key Parameters | Response Structure |
|------|---------------|-------------------|
| `ReactomeAnalysis_pathway_enrichment` | `identifiers: str` (space-separated, NOT array) | 

…(truncated)