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

• Report-first approach - Create report file FIRST, then populate progressively

• Entity disambiguation FIRST - Resolve all identifiers before analysis

• Bidirectional network - Construct C-T-D network comprehensively from both directions

• Network metrics - Calculate proximity, centrality, module overlap quantitatively

• Rank candidates - Prioritize by composite Network Pharmacology Score

• Mechanism prediction - Explain HOW drug could work for disease via network paths

• Clinical feasibility - FDA-approved drugs ranked higher than preclinical

• Safety context - Flag known adverse events and off-target liabilities

• Evidence grading - Grade all evidence T1-T4

• Negative results documented - "No data" is data; empty sections are failures

• Source references - Every finding must cite the source tool/database

• 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.

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.

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.

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).

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.

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).

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).

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).

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Network Pharmacology Analysis: [Entity]

Executive Summary

[2-3 sentence summary of key findings]

Network Pharmacology Score: [X]/100 - [Tier]

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

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

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) {data: {pathways: [{pathway_id, name, p_value, fdr, entities_found}]}}

enrichr_gene_enrichment_analysis

gene_list: list[str] , libs: list[str] (REQUIRED) Enrichment per library

drugbank_get_pathways_reactions_by_drug_or_id

query, case_sensitive, exact_match, limit

Pathway data

Safety Tools

Tool Key Parameters Response Structure

FAERS_calculate_disproportionality

operation: str , drug_name: str , adverse_event: str

{metrics: {PRR, ROR, IC}, signal_detection}

FAERS_filter_serious_events

operation: str , drug_name: str , seriousness_type: str

Serious event data

FAERS_count_death_related_by_drug

medicinalproduct: str

[{term, count}]

OpenTargets_get_drug_adverse_events_by_chemblId

chemblId: str

{data: {drug: {adverseEvents: {count, rows}}}}

OpenTargets_get_drug_warnings_by_chemblId

chemblId: str

Drug warning data

OpenTargets_get_target_safety_profile_by_ensemblID

ensemblId: str

Target safety data

gnomad_get_gene_constraints

gene_symbol: str

Gene constraint (pLI, LOEUF)

FDA_get_warnings_and_cautions_by_drug_name

drug_name: str

FDA warning text

Literature Tools

Tool Key Parameters Response Structure

PubMed_search_articles

query: str , max_results: int

list of {pmid, title, authors, journal, pub_date}

EuropePMC_search_articles

query: str , limit: int

Article list

OpenTargets_get_publications_by_drug_chemblId

chemblId: str , size: int

Publication data

Response Format Notes

DrugBank tools: ALL require query , case_sensitive , exact_match , limit (4 params, ALL required).

FAERS analytics tools (disproportionality, compare, filter, stratify, rollup, trends): ALL require operation parameter.

FAERS count tools (count_death, count_reactions, etc.): Use medicinalproduct NOT drug_name .

OpenTargets tools: Return nested {data: {entity: {field: ...}}} structure.

PubMed_search_articles: Returns plain list of dicts, NOT {articles: [...]} .

PubChem CID lookup: Returns {IdentifierList: {CID: [...]}} (NO data wrapper).

ReactomeAnalysis_pathway_enrichment: Takes space-separated identifiers string, NOT array.

ensembl_lookup_gene: REQUIRES species='homo_sapiens' parameter.

STRING tools: Return {status: "success", data: [...]} .

CTD tools: Return {data: [...]} with potentially large result sets.

Fallback Strategies

Phase Primary Tool Fallback 1 Fallback 2

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 clinical_trials_search PubMed clinical

Safety FAERS + FDA OpenTargets AEs DrugBank safety

Literature PubMed search EuropePMC search OpenTargets publications

Common Use Patterns

Pattern 1: Drug Repurposing via Network Proximity

Input: compound (metformin) + disease (Alzheimer disease) Mode: compound-to-disease

Flow:

1. Resolve metformin -> CHEMBL1431, DB00331, CID:4091
2. Get metformin targets (OpenTargets, DrugBank, DGIdb)
3. Get Alzheimer disease genes (OpenTargets, GWAS)
4. Build PPI network (STRING, OpenTargets interactions)
5. Calculate proximity between drug targets and disease genes
6. Score and rank by Network Pharmacology Score
7. Predict mechanism via shared pathways
8. Validate with clinical trials and literature

Pattern 2: Disease-Driven Drug Discovery

Input: disease (lupus) Mode: disease-to-compound

Flow:

1. Resolve lupus -> MONDO/EFO ID
2. Get disease-associated targets (top 50)
3. For each target, find approved drugs (OpenTargets, DGIdb, DrugBank)
4. Build C-T-D network from all drug-target-disease edges
5. Rank drugs by: number of disease targets hit, network proximity, safety
6. Identify polypharmacology advantages (drugs hitting multiple disease targets)

Pattern 3: Target-Centric Network

Input: target (EGFR) Mode: target-centric

Flow:

1. Resolve EGFR -> ENSG00000146648
2. Get all compounds targeting EGFR (with bioactivity)
3. Get all diseases associated with EGFR
4. Build PPI network around EGFR
5. Identify which compounds could bridge to which diseases
6. Rank compound-disease pairs by network metrics

Pattern 4: Polypharmacology Profiling

Input: compound (aspirin) Mode: bidirectional

Flow:

1. Resolve aspirin -> CHEMBL25
2. Get ALL targets (not just primary)
3. Map targets to disease modules
4. Identify multi-target coverage across diseases
5. Analyze synergistic vs antagonistic effects
6. Compare selectivity across target families

Pattern 5: Mechanism Elucidation

Input: compound (rapamycin) + disease (aging/longevity) Mode: compound-to-disease

Flow:

1. Resolve rapamycin -> CHEMBL413 (sirolimus)
2. Get mechanism: mTOR inhibitor
3. Map mTOR pathway to aging-related genes
4. Trace network paths: rapamycin -> mTOR -> autophagy -> aging genes
5. Assess pathway overlap and functional enrichment
6. Provide mechanistic explanation

Edge Cases

Promiscuous Compounds (many targets)

• Limit initial target retrieval to top 50 by confidence

• Classify into primary (mechanism) vs secondary (off-target)

• Focus network analysis on primary targets first

• Note polypharmacology implications

Orphan Diseases (limited data)

• Expand to parent disease categories in ontology

• Use related diseases from OpenTargets similar entities

• Leverage pathway-level analysis over gene-level

• Note data limitations in report

Novel Targets (no known drugs)

• Focus on target biology and disease association

• Use DGIdb druggability assessment

• Search for chemical probes (OpenTargets chemical probes)

• Suggest target-based screening approaches

Large Networks (>100 nodes)

• Prioritize top-scored edges

• Use network modules rather than full network

• Focus on shortest paths between entities

• Summarize statistics rather than listing all nodes

Disconnected Networks

• Report disconnection explicitly

• Analyze drug module and disease module separately

• Look for pathway-level connections as bridge

• Note that disconnection suggests low repurposing potential

Troubleshooting

"Disease not found":

• Try disease synonyms (e.g., "Alzheimer's disease" vs "Alzheimer disease")

• Use EFO/MONDO ID directly if known

• Search with OpenTargets_multi_entity_search_by_query_string(queryString=...) for broader matching

• Try parent disease category

"No drugs found for target":

• Target may be Tdark (no chemical tools) - check with Pharos

• Expand to target family or pathway

• Search DGIdb which aggregates multiple sources

• Check chemical probes as starting points

"No PPI data":

• Try different protein identifiers (gene symbol, UniProt, Ensembl protein)

• Use multiple PPI databases (STRING + IntAct + OpenTargets)

• Lower confidence threshold in STRING

• Use pathway co-membership as proxy for interaction

"Network proximity not significant":

• Drug targets may be functionally distant from disease module

• Try expanding disease gene set (increase limit)

• Consider indirect mechanisms via shared pathways

• Report honestly - not all drug-disease pairs have network support

"DrugBank parameter errors":

• ALL DrugBank tools require 4 params: query , case_sensitive , exact_match , limit

• Use case_sensitive=False , exact_match=True for exact drug name matching

• Use exact_match=False for broader searches

"FAERS operation errors":

• Analytics tools (disproportionality, compare, filter, stratify) need operation param

• Count tools use medicinalproduct NOT drug_name

• Check FAERS tool name carefully to determine which pattern

Resources

For focused drug repurposing (without network analysis): tooluniverse-drug-repurposing For target validation: tooluniverse-drug-target-validation For adverse event detection: tooluniverse-adverse-event-detection For systems biology: tooluniverse-systems-biology For protein interactions: tooluniverse-protein-interactions