Drug Repurposing with ToolUniverse

Systematically identify and evaluate drug repurposing candidates using multiple computational strategies.

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.

Core Strategies

Target-Based Repurposing

Start with disease targets → Find drugs that modulate those targets

Compound-Based Repurposing

Start with approved drugs → Find new disease indications

Disease-Driven Repurposing

Start with disease → Find targets → Match to existing drugs

Quick Start

from tooluniverse import ToolUniverse

tu = ToolUniverse(use_cache=True) tu.load_tools()

Example: Find repurposing candidates for a disease

disease_name = "rheumatoid arthritis"

Step 1: Get disease information

disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name( diseaseName=disease_name )

Step 2: Get associated targets

disease_id = disease_info['data']['id'] targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId( efoId=disease_id, limit=10 )

Step 3: Find drugs for each target

for target in targets['data'][:5]: drugs = tu.tools.DGIdb_get_drug_gene_interactions( gene_name=target['gene_symbol'] ) # Evaluate each drug candidate...

Complete Workflow

Phase 1: Disease & Target Analysis

1.1 Get disease information

disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name( diseaseName="[disease_name]" )

1.2 Find associated targets

targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId( efoId=disease_info['data']['id'], limit=20 )

1.3 Get target details for top candidates

target_details = [] for target in targets['data'][:10]: details = tu.tools.UniProt_get_entry_by_accession( accession=target['uniprot_id'] ) target_details.append(details)

Phase 2: Drug Discovery

2.1 Find drugs targeting disease-associated targets

drug_candidates = []

for target in targets['data'][:10]: # Search DrugBank drugbank_results = tu.tools.drugbank_get_drug_name_and_description_by_target_name( target_name=target['gene_symbol'] )

# Search DGIdb
dgidb_results = tu.tools.DGIdb_get_drug_gene_interactions(
    gene_name=target['gene_symbol']
)

# Search ChEMBL
chembl_results = tu.tools.ChEMBL_search_drugs(
    query=target['gene_symbol'],
    limit=10
)

drug_candidates.extend([drugbank_results, dgidb_results, chembl_results])

2.2 Get drug details

for drug_name in unique_drugs: # Get DrugBank info drug_info = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id( drug_name_or_drugbank_id=drug_name )

# Get current indications
indications = tu.tools.drugbank_get_indications_by_drug_name_or_drugbank_id(
    drug_name_or_drugbank_id=drug_name
)

# Get pharmacology
pharmacology = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id(
    drug_name_or_drugbank_id=drug_name
)

Phase 3: Safety & Feasibility Assessment

3.1 Check FDA safety data

for drug in top_candidates: # Get warnings and precautions warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name( drug_name=drug['name'] )

# Get adverse event reports
adverse_events = tu.tools.FAERS_search_reports_by_drug_and_reaction(
    drug_name=drug['name'],
    limit=100
)

# Get drug interactions
interactions = tu.tools.drugbank_get_drug_interactions_by_drug_name_or_id(
    drug_name_or_id=drug['name']
)

3.2 Assess ADMET properties (for novel formulations)

for drug in top_candidates: if 'smiles' in drug: admet = tu.tools.ADMETAI_predict_admet( smiles=drug['smiles'], use_cache=True )

Phase 4: Literature Evidence

4.1 Search for existing evidence

for drug in top_candidates: # PubMed search query = f"{drug['name']} AND {disease_name}" pubmed_results = tu.tools.PubMed_search_articles( query=query, max_results=50 )

# Europe PMC search
pmc_results = tu.tools.EuropePMC_search_articles(
    query=query,
    limit=50
)

# Clinical trials
trials = tu.tools.ClinicalTrials_search(
    condition=disease_name,
    intervention=drug['name']
)

Phase 5: Scoring & Ranking

Create a scoring function to rank candidates:

def score_repurposing_candidate(drug, target_score, safety_data, literature_count): """Score drug repurposing candidate (0-100).""" score = 0

# Target association strength (0-40 points)
score += min(target_score * 40, 40)

# Safety profile (0-30 points)
if drug['approval_status'] == 'approved':
    score += 20
elif drug['approval_status'] == 'clinical':
    score += 10

if not safety_data.get('black_box_warning'):
    score += 10

# Literature evidence (0-20 points)
score += min(literature_count / 5 * 20, 20)

# Drug-likeness (0-10 points)
if drug.get('bioavailability') == 'high':
    score += 10

return score

Score all candidates

scored_candidates = [] for drug in drug_candidates: score = score_repurposing_candidate( drug=drug, target_score=drug['target_association_score'], safety_data=drug['safety_profile'], literature_count=drug['supporting_papers'] ) drug['repurposing_score'] = score scored_candidates.append(drug)

Sort by score

ranked_candidates = sorted( scored_candidates, key=lambda x: x['repurposing_score'], reverse=True )

Alternative Strategies

Strategy A: Mechanism-Based Repurposing

Find drugs with similar mechanism of action

known_drug = "metformin"

Get mechanism

moa = tu.tools.drugbank_get_drug_desc_pharmacology_by_moa( mechanism_of_action="[moa_term]" )

Get similar drugs

similar = tu.tools.ChEMBL_search_similar_molecules( query=known_drug, similarity_threshold=70 )

Strategy B: Network-Based Repurposing

Use pathway analysis

pathways = tu.tools.drugbank_get_pathways_reactions_by_drug_or_id( drug_name_or_drugbank_id="[drug_name]" )

Find drugs affecting same pathways

pathway_drugs = tu.tools.drugbank_get_drug_name_and_description_by_pathway_name( pathway_name=pathways['data'][0]['pathway_name'] )

Strategy C: Phenotype-Based Repurposing

Search by indication/phenotype

indication_drugs = tu.tools.drugbank_get_drug_name_and_description_by_indication( indication="[related_indication]" )

Analyze adverse events as therapeutic effects

Example: minoxidil (hypertension) → hair growth

adverse_as_therapeutic = tu.tools.FAERS_search_reports_by_drug_and_reaction( drug_name="[drug_name]", limit=1000 )

Key ToolUniverse Tools

Disease & Target Tools:

OpenTargets_get_disease_id_description_by_name
Disease lookup
OpenTargets_get_associated_targets_by_disease_efoId
Disease targets
UniProt_get_entry_by_accession
Protein details

Drug Discovery Tools:

drugbank_get_drug_name_and_description_by_target_name
Drugs by target
drugbank_get_drug_name_and_description_by_indication
Drugs by indication
DGIdb_get_drug_gene_interactions
Drug-gene interactions
ChEMBL_search_drugs
Drug search
ChEMBL_get_drug_mechanisms
Mechanism of action

Drug Information Tools:

drugbank_get_drug_basic_info_by_drug_name_or_id
Basic drug info
drugbank_get_indications_by_drug_name_or_drugbank_id
Approved indications
drugbank_get_pharmacology_by_drug_name_or_drugbank_id
Pharmacology
drugbank_get_targets_by_drug_name_or_drugbank_id
Drug targets

Safety Assessment Tools:

FDA_get_warnings_and_cautions_by_drug_name
FDA warnings
FAERS_search_reports_by_drug_and_reaction
Adverse events
FAERS_count_death_related_by_drug
Serious outcomes
drugbank_get_drug_interactions_by_drug_name_or_id
Interactions

Property Prediction Tools:

ADMETAI_predict_admet
ADMET properties
ADMETAI_predict_toxicity
Toxicity prediction

Literature Tools:

PubMed_search_articles
PubMed search
EuropePMC_search_articles
Europe PMC search
ClinicalTrials_search
Clinical trials

Output Format

Present results as ranked candidates:

Drug Repurposing Analysis: [Disease Name]

Top 10 Repurposing Candidates

1. [Drug Name] (Score: 87/100)

Current Indications: [list approved uses] Proposed Indication: [new disease/condition] Repurposing Rationale: Targets [gene/protein] with high association to disease

Evidence Summary:

Target association score: 0.85
Approval status: FDA approved (safer profile)
Literature support: 23 papers, 4 clinical trials
Safety profile: No black box warnings

Mechanism: [Brief mechanism description]

Next Steps:

Phase II trial feasibility assessment
Patient population identification
Dosing optimization study

Key Papers:

Smith et al. 2024 - Clinical efficacy in similar condition
Jones et al. 2023 - Mechanism validation

2. [Drug Name] (Score: 79/100)

[Similar structure...]

Supporting Analysis

Target Network: [visualization or description] Pathway Overlap: [affected pathways] Safety Considerations: [major concerns] Development Timeline: [estimated phases]

Scoring Criteria

Target Association (0-40 points):

Strong genetic evidence: 40
Moderate association: 25
Pathway-level evidence: 15
Weak/predicted: 5

Safety Profile (0-30 points):

FDA approved: 20
Phase III: 15
Phase II: 10
Phase I: 5
No black box warning: +10
Known serious AE: -10

Literature Evidence (0-20 points):

Clinical trials: 5 points each (max 15)
Preclinical studies: 1 point each (max 10)
Case reports: 0.5 points each (max 5)

Drug Properties (0-10 points):

High bioavailability: 5
Good BBB penetration (if CNS): 5
Low toxicity predictions: 5

Best Practices

Start Broad: Query multiple databases (DrugBank, ChEMBL, DGIdb)
Validate Targets: Confirm target-disease associations in OpenTargets
Check Safety First: Prioritize approved drugs with known safety profiles
Literature Mining: Always search for existing clinical/preclinical evidence
Use Caching: Enable use_cache=True for expensive predictions
Batch Operations: Use tu.run_batch() for parallel queries
Consider Mechanism: Evaluate biological plausibility
Patent Landscape: Check if indication is already protected
Market Analysis: Consider unmet medical need and commercial viability
Regulatory Path: FDA approved drugs have faster repurposing path

Common Patterns

Pattern 1: Rapid Screening

Quick screening of 100+ drugs against disease targets

targets = get_disease_targets(disease_id)[:10] all_drugs = []

for target in targets: drugs = tu.tools.DGIdb_get_drug_gene_interactions( gene_name=target['gene_symbol'] ) all_drugs.extend(drugs)

Filter to FDA approved only

approved_drugs = [d for d in all_drugs if d.get('approved')]

Pattern 2: Deep Dive Single Drug

Comprehensive analysis of one drug candidate

drug_name = "metformin"

Get everything

info = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id(drug_name_or_drugbank_id=drug_name) targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id(drug_name_or_drugbank_id=drug_name) indications = tu.tools.drugbank_get_indications_by_drug_name_or_drugbank_id(drug_name_or_drugbank_id=drug_name) pharmacology = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id(drug_name_or_drugbank_id=drug_name) interactions = tu.tools.drugbank_get_drug_interactions_by_drug_name_or_id(drug_name_or_id=drug_name) warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name(drug_name=drug_name) papers = tu.tools.PubMed_search_articles(query=f"{drug_name} AND [new_disease]", max_results=100)

Pattern 3: Comparative Analysis

Compare multiple candidates side-by-side

candidates = ["drug_a", "drug_b", "drug_c"]

comparison = [] for drug in candidates: data = { 'name': drug, 'info': tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id(drug_name_or_drugbank_id=drug), 'safety': tu.tools.FDA_get_warnings_and_cautions_by_drug_name(drug_name=drug), 'evidence': tu.tools.PubMed_search_articles(query=drug, max_results=10) } comparison.append(data)

Troubleshooting

"Disease not found":

Try disease synonyms or EFO ID lookup
Use broader disease categories

"No drugs found for target":

Check target name/symbol (HUGO nomenclature)
Expand to pathway-level drugs
Consider similar targets (protein family)

"Insufficient literature evidence":

Search for drug class rather than specific drug
Check preclinical/animal studies
Look for mechanism papers

"Safety data unavailable":

Drug may not be FDA approved in US
Check EMA or other regulatory databases
Review clinical trial safety data

Example Use Cases

Use Case 1: Find repurposing candidates for rare disease

Rare disease often lack approved drugs

Strategy: Find drugs targeting same pathways as related common diseases

rare_disease = "Niemann-Pick disease" related_disease = "Alzheimer's disease" # Similar pathology

Get pathways affected in related disease

targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId( efoId=related_disease_id )

Find drugs for those targets

Evaluate for rare disease applicability

Use Case 2: Repurpose based on adverse effects

Adverse effect in one context = therapeutic in another

Example: Thalidomide (teratogenic) → cancer treatment

drug = "drug_name" adverse_events = tu.tools.FAERS_search_reports_by_drug_and_reaction( drug_name=drug, limit=1000 )

Analyze if adverse effects beneficial in other contexts

Example: weight loss AE → obesity treatment potential

Use Case 3: Combination therapy discovery

Find drugs that complement existing therapy

primary_drug = "existing_therapy" disease = "disease_name"

Get targets not covered by primary drug

disease_targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId( efoId=disease_id )

primary_targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=primary_drug )

Find drugs for uncovered targets

uncovered_targets = [t for t in disease_targets if t not in primary_targets]

Advanced Techniques

Technique 1: Polypharmacology-Based Repurposing

Find drugs with multi-target activity matching disease network

Get disease network

targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId( efoId=disease_id, limit=50 )

For each drug, count how many disease targets it hits

for drug in candidate_drugs: drug_targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=drug )

overlap = len(set(drug_targets) &#x26; set(disease_targets))
if overlap >= 3:  # Multi-target match
    print(f"{drug}: hits {overlap} disease targets")

Technique 2: Structure-Based Repurposing

Find structurally similar approved drugs

known_active = "known_active_compound"

Get structure

cid = tu.tools.PubChem_get_CID_by_compound_name( compound_name=known_active )

Find similar

similar = tu.tools.PubChem_search_compounds_by_similarity( cid=cid['data']['cid'], threshold=85 )

Check which are approved drugs

for compound in similar['data']: drug_info = tu.tools.PubChem_get_drug_label_info_by_CID( cid=compound['cid'] )

Technique 3: AI-Powered Candidate Selection

Use ML predictions to filter candidates

candidates_with_smiles = get_candidates_with_structures()

Predict ADMET for all

admet_results = [] for drug in candidates_with_smiles: admet = tu.tools.ADMETAI_predict_admet( smiles=drug['smiles'], use_cache=True ) admet_results.append({ 'drug': drug['name'], 'admet': admet, 'pass': evaluate_admet_criteria(admet) })

Keep only drugs passing ADMET criteria

viable_candidates = [r for r in admet_results if r['pass']]

Resources

For comprehensive disease analysis, see disease-intelligence-gatherer skill.

For compound property analysis, see chemical-compound-retrieval skill.

For detailed ToolUniverse SDK usage, see tooluniverse-sdk skill.