Drug Repurposing with ToolUniverse

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

IMPORTANT: Always use English terms in tool calls. Respond in the user's language.

Core Strategies

1. Target-Based: Disease targets -> Find drugs that modulate those targets
2. Compound-Based: Approved drugs -> Find new disease indications
3. Disease-Driven: Disease -> Targets -> Match to existing drugs

Workflow Overview

Phase 1: Disease & Target Analysis
  Get disease info (OpenTargets), find associated targets, get target details

Phase 2: Drug Discovery
  Search DrugBank, DGIdb, ChEMBL for drugs targeting disease-associated genes
  Get drug details, indications, pharmacology

Phase 3: Safety & Feasibility Assessment
  FDA warnings, FAERS adverse events, drug interactions, ADMET predictions

Phase 4: Literature Evidence
  PubMed, Europe PMC, clinical trials for existing evidence

Phase 5: Scoring & Ranking
  Composite score: target association + safety + literature + drug properties

See: PROCEDURES.md for detailed step-by-step procedures and code patterns.

Quick Start

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

# Step 1: Get disease targets
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName="rheumatoid arthritis")
targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(efoId=disease_info['data']['id'], limit=10)

# Step 2: Find drugs for each target
for target in targets['data'][:5]:
    drugs = tu.tools.DGIdb_get_drug_gene_interactions(gene_name=target['gene_symbol'])

Key ToolUniverse Tools

Disease & Target:

• 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:

• 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 / ChEMBL_get_drug_mechanisms - Drug search and MOA

Drug Information:

• drugbank_get_drug_basic_info_by_drug_name_or_id - Basic 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:

• 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:

• ADMETAI_predict_admet / ADMETAI_predict_toxicity - ADMET and toxicity

Literature:

• PubMed_search_articles / EuropePMC_search_articles / ClinicalTrials_search

Scoring Criteria

Best Practices

1. Start broad: Query multiple databases (DrugBank, ChEMBL, DGIdb)
2. Validate targets: Confirm target-disease associations in OpenTargets
3. Safety first: Prioritize approved drugs with known safety profiles
4. Literature mining: Search for existing clinical/preclinical evidence
5. Consider mechanism: Evaluate biological plausibility
6. Batch operations: Use tu.run_batch() for parallel queries

Troubleshooting

Reference Files

• REFERENCE.md - Detailed reference documentation
• EXAMPLES.md - Sample repurposing analyses
• PROCEDURES.md - Step-by-step procedures with code
• REPORT_TEMPLATE.md - Output report template
• Related skills: disease-intelligence-gatherer, chemical-compound-retrieval, tooluniverse-sdk