opentargets-database

•

24.4k23993 months ago

Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.

Quick Install

npxskills add davila7/claude-code-templates--skill opentargets-database

Instructions

Open Targets Database

Overview

The Open Targets Platform is a comprehensive resource for systematic identification and prioritization of potential therapeutic drug targets. It integrates publicly available datasets including human genetics, omics, literature, and chemical data to build and score target-disease associations.

Key capabilities:

Query target (gene) annotations including tractability, safety, expression
Search for disease-target associations with evidence scores
Retrieve evidence from multiple data types (genetics, pathways, literature, etc.)
Find known drugs for diseases and their mechanisms
Access drug information including clinical trial phases and adverse events
Evaluate target druggability and therapeutic potential

Data access: The platform provides a GraphQL API, web interface, data downloads, and Google BigQuery access. This skill focuses on the GraphQL API for programmatic access.

When to Use This Skill

This skill should be used when:

Target discovery: Finding potential therapeutic targets for a disease
Target assessment: Evaluating tractability, safety, and druggability of genes
Evidence gathering: Retrieving supporting evidence for target-disease associations
Drug repurposing: Identifying existing drugs that could be repurposed for new indications
Competitive intelligence: Understanding clinical precedence and drug development landscape
Target prioritization: Ranking targets based on genetic evidence and other data types
Mechanism research: Investigating biological pathways and gene functions
Biomarker discovery: Finding genes differentially expressed in disease
Safety assessment: Identifying potential toxicity concerns for drug targets

Core Workflow

1. Search for Entities

Start by finding the identifiers for targets, diseases, or drugs of interest.

For targets (genes):

from scripts.query_opentargets import search_entities

# Search by gene symbol or name
results = search_entities("BRCA1", entity_types=["target"])
# Returns: [{"id": "ENSG00000012048", "name": "BRCA1", ...}]

For diseases:

# Search by disease name
results = search_entities("alzheimer", entity_types=["disease"])
# Returns: [{"id": "EFO_0000249", "name": "Alzheimer disease", ...}]

For drugs:

# Search by drug name
results = search_entities("aspirin", entity_types=["drug"])
# Returns: [{"id": "CHEMBL25", "name": "ASPIRIN", ...}]

Identifiers used:

Targets: Ensembl gene IDs (e.g., ENSG00000157764)
Diseases: EFO (Experimental Factor Ontology) IDs (e.g., EFO_0000249)
Drugs: ChEMBL IDs (e.g., CHEMBL25)

2. Query Target Information

Retrieve comprehensive target annotations to assess druggability and biology.

from scripts.query_opentargets import get_target_info

target_info = get_target_info("ENSG00000157764", include_diseases=True)

# Access key fields:
# - approvedSymbol: HGNC gene symbol
# - approvedName: Full gene name
# - tractability: Druggability assessments across modalities
# - safetyLiabilities: Known safety concerns
# - geneticConstraint: Constraint scores from gnomAD
# - associatedDiseases: Top disease associations with scores

Key annotations to review:

Tractability: Small molecule, antibody, PROTAC druggability predictions
Safety: Known toxicity concerns from multiple databases
Genetic constraint: pLI and LOEUF scores indicating essentiality
Disease associations: Diseases linked to the target with evidence scores

Refer to references/target_annotations.md for detailed information about all target features.

3. Query Disease Information

Get disease details and associated targets/drugs.

from scripts.query_opentargets import get_disease_info

disease_info = get_disease_info("EFO_0000249", include_targets=True)

# Access fields:
# - name: Disease name
# - description: Disease description
# - therapeuticAreas: High-level disease categories
# - associatedTargets: Top targets with association scores

4. Retrieve Target-Disease Evidence

Get detailed evidence supporting a target-disease association.

from scripts.query_opentargets import get_target_disease_evidence

# Get all evidence
evidence = get_target_disease_evidence(
    ensembl_id="ENSG00000157764",
    efo_id="EFO_0000249"
)

# Filter by evidence type
genetic_evidence = get_target_disease_evidence(
    ensembl_id="ENSG00000157764",
    efo_id="EFO_0000249",
    data_types=["genetic_association"]
)

# Each evidence record contains:
# - datasourceId: Specific data source (e.g., "gwas_catalog", "chembl")
# - datatypeId: Evidence category (e.g., "genetic_association", "known_drug")
# - score: Evidence strength (0-1)
# - studyId: Original study identifier
# - literature: Associated publications

Major evidence types:

genetic_association: GWAS, rare variants, ClinVar, gene burden
somatic_mutation: Cancer Gene Census, IntOGen, cancer biomarkers
known_drug: Clinical precedence from approved/clinical drugs
affected_pathway: CRISPR screens, pathway analyses, gene signatures
rna_expression: Differential expression from Expression Atlas
animal_model: Mouse phenotypes from IMPC
literature: Text-mining from Europe PMC

Refer to references/evidence_types.md for detailed descriptions of all evidence types and interpretation guidelines.

5. Find Known Drugs

Identify drugs used for a disease and their targets.

from scripts.query_opentargets import get_known_drugs_for_disease

drugs = get_known_drugs_for_disease("EFO_0000249")

# drugs contains:
# - uniqueDrugs: Total number of unique drugs
# - uniqueTargets: Total number of unique targets
# - rows: List of drug-target-indication records with:
#   - drug: {name, drugType, maximumClinicalTrialPhase}
#   - targets: Genes targeted by the drug
#   - phase: Clinical trial phase for this indication
#   - status: Trial status (active, completed, etc.)
#   - mechanismOfAction: How drug works

Clinical phases:

Phase 4: Approved drug
Phase 3: Late-stage clinical trials
Phase 2: Mid-stage trials
Phase 1: Early safety trials

6. Get Drug Information

Retrieve detailed drug information including mechanisms and indications.

from scripts.query_opentargets import get_drug_info

drug_info = get_drug_info("CHEMBL25")

# Access:
# - name, synonyms: Drug identifiers
# - drugType: Small molecule, antibody, etc.
# - maximumClinicalTrialPhase: Development stage
# - mechanismsOfAction: Target and action type
# - indications: Diseases with trial phases
# - withdrawnNotice: If withdrawn, reasons and countries

7. Get All Associations for a Target

Find all diseases associated with a target, optionally filtering by score.

from scripts.query_opentargets import get_target_associations

# Get associations with score >= 0.5
associations = get_target_associations(
    ensembl_id="ENSG00000157764",
    min_score=0.5
)

# Each association contains:
# - disease: {id, name}
# - score: Overall association score (0-1)
# - datatypeScores: Breakdown by evidence type

Association scores:

Range: 0-1 (higher = stronger evidence)
Aggregate evidence across all data types using harmonic sum
NOT confidence scores but relative ranking metrics
Under-studied diseases may have lower scores despite good evidence

GraphQL API Details

For custom queries beyond the provided helper functions, use the GraphQL API directly or modify scripts/query_opentargets.py.

Key information:

Endpoint: https://api.platform.opentargets.org/api/v4/graphql
Interactive browser: https://api.platform.opentargets.org/api/v4/graphql/browser
No authentication required
Request only needed fields to minimize response size
Use pagination for large result sets: page: {size: N, index: M}

Refer to references/api_reference.md for:

Complete endpoint documentation
Example queries for all entity types
Error handling patterns
Best practices for API usage

Best Practices

Target Prioritization Strategy

When prioritizing drug targets:

Start with genetic evidence: Human genetics (GWAS, rare variants) provides strongest disease relevance
Check tractability: Prefer targets with clinical or discovery precedence
Assess safety: Review safety liabilities, expression patterns, and genetic constraint
Evaluate clinical precedence: Known drugs indicate druggability and therapeutic window
Consider multiple evidence types: Convergent evidence from different sources increases confidence
Validate mechanistically: Pathway evidence and biological plausibility
Review literature manually: For critical decisions, examine primary publications

Evidence Interpretation

Strong evidence indicators:

Multiple independent evidence sources
High genetic association scores (especially GWAS with L2G > 0.5)
Clinical precedence from approved drugs
ClinVar pathogenic variants with disease match
Mouse models with relevant phenotypes

Caution flags:

Single evidence source only
Text-mining as sole evidence (requires manual validation)
Conflicting evidence across sources
High essentiality + ubiquitous expression (poor therapeutic window)
Multiple safety liabilities

Score interpretation:

Scores rank relative strength, not absolute confidence
Under-studied diseases have lower scores despite potentially valid targets
Weight expert-curated sources higher than computational predictions
Check evidence breakdown, not just overall score

Common Workflows

Workflow 1: Target Discovery for a Disease

Search for disease → get EFO ID
Query disease info with include_targets=True
Review top targets sorted by association score
For promising targets, get detailed target info
Examine evidence types supporting each association
Assess tractability and safety for prioritized targets

Workflow 2: Target Validation

Search for target → get Ensembl ID
Get comprehensive target info
Check tractability (especially clinical precedence)
Review safety liabilities and genetic constraint
Examine disease associations to understand biology
Look for chemical probes or tool compounds
Check known drugs targeting gene for mechanism insights

Workflow 3: Drug Repurposing

Search for disease → get EFO ID
Get known drugs for disease
For each drug, get detailed drug info
Examine mechanisms of action and targets
Look for related disease indications
Assess clinical trial phases and status
Identify repurposing opportunities based on mechanism

Workflow 4: Competitive Intelligence

Search for target of interest
Get associated diseases with evidence
For each disease, get known drugs
Review clinical phases and development status
Identify competitors and their mechanisms
Assess clinical precedence and market landscape

Resources

Scripts

scripts/query_opentargets.py Helper functions for common API operations:

search_entities() - Search for targets, diseases, or drugs
get_target_info() - Retrieve target annotations
get_disease_info() - Retrieve disease information
get_target_disease_evidence() - Get supporting evidence
get_known_drugs_for_disease() - Find drugs for a disease
get_drug_info() - Retrieve drug details
get_target_associations() - Get all associations for a target
execute_query() - Execute custom GraphQL queries

References

references/api_reference.md Complete GraphQL API documentation including:

Endpoint details and authentication
Available query types (target, disease, drug, search)
Example queries for all common operations
Error handling and best practices
Data licensing and citation requirements

references/evidence_types.md Comprehensive guide to evidence types and data sources:

Detailed descriptions of all 7 major evidence types
Scoring methodologies for each source
Evidence interpretation guidelines
Strengths and limitations of each evidence type
Quality assessment recommendations

references/target_annotations.md Complete target annotation reference:

12 major annotation categories explained
Tractability assessment details
Safety liability sources
Expression, essentiality, and constraint data
Interpretation guidelines for target prioritization
Red flags and green flags for target assessment

Data Updates and Versioning

The Open Targets Platform is updated quarterly with new data releases. The current release (as of October 2025) is available at the API endpoint.

Release information: Check https://platform-docs.opentargets.org/release-notes for the latest updates.

Citation: When using Open Targets data, cite: Ochoa, D. et al. (2025) Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research, 53(D1):D1467-D1477.

Limitations and Considerations

API is for exploratory queries: For systematic analyses of many targets/diseases, use data downloads or BigQuery
Scores are relative, not absolute: Association scores rank evidence strength but don't predict clinical success
Under-studied diseases score lower: Novel or rare diseases may have strong evidence but lower aggregate scores
Evidence quality varies: Weight expert-curated sources higher than computational predictions
Requires biological interpretation: Scores and evidence must be interpreted in biological and clinical context
No authentication required: All data is freely accessible, but cite appropriately

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