opentargets-py 0.1.0

Unofficial community-maintained Python client for the Open Targets Platform GraphQL API

opentargets-py

Modern Python client for the Open Targets Platform GraphQL API.

Disclaimer: This is an unofficial, community-maintained client and is not affiliated with, endorsed by, or supported by the Open Targets consortium. For the official platform, visit platform.opentargets.org.

The official opentargets package was deprecated when Open Targets migrated to GraphQL in 2021 and has since been removed from PyPI. This library fills that gap — it is the only Python SDK targeting the current GraphQL API.

Installation

pip install opentargets-py

With pandas support:

pip install opentargets-py[pandas]

Quick Start

from opentargets import OpenTargetsClient

client = OpenTargetsClient()

# Look up a target by gene symbol
target = client.get_target("EGFR")
print(target.approved_name)  # epidermal growth factor receptor

# Get associated diseases
associations = client.get_target_associations("EGFR", limit=10)
for a in associations:
    print(a.disease_name, a.score)

# Get drugs for a target
drugs = client.get_target_drugs("EGFR")

# Look up a disease
disease = client.get_disease("EFO_0003060")

# Search across the platform
results = client.search("lung cancer", entity_type="disease", limit=5)

Features

• Type-safe — full Pydantic v2 models, py.typed marker, mypy --strict compliant
• Symbol resolution — pass "EGFR" instead of "ENSG00000146648"
• Auto-pagination — fetches all pages transparently
• In-memory cache — LRU cache with TTL, reduces redundant API calls
• Retry with backoff — automatic retries on 429/5xx with exponential backoff
• Pandas integration — as_dataframe=True on any list method
• Minimal dependencies — only httpx and pydantic

More Examples

See the examples/ directory:

• basic_usage.py — targets, diseases, drugs, search
• batch_query.py — batch fetch + DataFrame output
• network_analysis.py — build a target–disease network

API Reference

See docs/api-reference.md.

Contributing

1. Fork the repo and create a feature branch.
2. Install dev dependencies: pip install -e ".[dev,pandas]"
3. Run tests: pytest
4. Run linting: ruff check src tests && ruff format src tests
5. Open a pull request.

License

Apache 2.0 — see LICENSE.