drugs 0.1.2

A short description

drugs

Lightweight Python utilities to work with small-molecule identifiers and metadata across PubChem and ChEMBL. The library exposes a single Drug class that lazily resolves identifiers (PubChem CID, ChEMBL ID, InChIKey), fetches PubChem properties/text, pulls ChEMBL mechanisms, and provides hooks for plugging in your own text or protein embedding functions with optional on-disk caching.

Highlights

• Lazy identifier translation between PubChem CID, ChEMBL ID, and InChIKey (via UniChem and PUG-REST)
• PubChem properties and PUG-View text retrieval with curated heading presets
• Structure representations: canonical SMILES + SELFIES
• Fingerprints (Morgan/MACCS/Daylight) with Tanimoto/Dice similarity + batch similarity matrices
• ChEMBL mechanisms, target details, and bioactivity rows (pChEMBL/IC50/EC50 filters)
• Drug-drug interactions via RxNav
• RDKit molecular property panel (QED, TPSA, Lipinski violations, synthetic accessibility)
• Embedding hooks for text and protein/sequence features, with simple caching helpers
• Markdown report generation for a drug snapshot

Installation

Python 3.9+ is required.

pip install -e .

For development (linting/tests/docs):

pip install -e ".[dev]"

Quick start

from drugs import Drug, PUBCHEM_MINIMAL_STABLE

# Start from any identifier
aspirin = Drug.from_pubchem_cid(2244)
# or: Drug.from_chembl_id("CHEMBL25") / Drug.from_inchikey("BSYNRYMUTXBXSQ-UHFFFAOYSA-N")

print(aspirin.map_ids())

props = aspirin.fetch_pubchem_properties()
text = aspirin.fetch_pubchem_text(PUBCHEM_MINIMAL_STABLE)
mechs = aspirin.fetch_chembl_mechanisms()
targets = aspirin.target_accessions()

# Structural views
print(aspirin.smiles())
print(aspirin.selfies())

# Fingerprints + similarity
fp = aspirin.molecular_fingerprint(method="morgan")
ibuprofen = Drug.from_chembl_id("CHEMBL521")
sim = aspirin.similarity_to(ibuprofen)

# Bioactivities and DDIs
acts = aspirin.fetch_chembl_bioactivities(min_pchembl=6.0, assay_types=["B", "F"])
ddis = aspirin.fetch_drug_interactions()

# Batch helpers
batch = Drug.from_batch([2244, "CHEMBL521", "BSYNRYMUTXBXSQ-UHFFFAOYSA-N"])
sim_matrix = Drug.batch_similarity_matrix(batch)

# RDKit property panel
print(aspirin.molecular_properties())

# Plug in your own embedding functions
vec = aspirin.text_embedding(lambda s: s.upper())  # replace with your model

# Write a markdown report
aspirin.write_drug_markdown(output_path="aspirin.md")

Caching

API responses (PubChem/ChEMBL/RxNav) are cached to artifacts/cache/api_cache.json by default with a 24h TTL. Configure via environment variables:

• DRUGS_CACHE_PATH – override cache path
• DRUGS_CACHE_TTL_SECONDS – TTL in seconds
• DRUGS_CACHE_DISABLED=1 – disable disk caching

API surface

• Drug.pubchem_cid, Drug.chembl_id, Drug.inchikey: resolved identifiers
• Drug.fetch_pubchem_properties(): dict of core PubChem properties
• Drug.fetch_pubchem_text(headings): filtered PUG-View text sections
• Structure: Drug.smiles(), Drug.selfies(), Drug.molecular_fingerprint(), Drug.similarity_to()
• Bioactivity/targets: Drug.fetch_chembl_mechanisms(), Drug.fetch_chembl_bioactivities(), Drug.fetch_target_details(), Drug.target_accessions(), Drug.target_gene_symbols()
• Safety: Drug.fetch_drug_interactions()
• RDKit properties: Drug.molecular_properties()
• Batch helpers: Drug.from_batch(), Drug.batch_similarity_matrix()
• Embedding helpers: text_embedding, text_embedding_cached, protein_embedding, protein_embedding_cached
• Reporting: write_drug_markdown

Heading presets

Curated heading sets live in drugs.constants (e.g., PUBCHEM_MINIMAL_STABLE, PUBCHEM_ADME_PK, PUBCHEM_MEANING, etc.). Use drugs.core.list_pubchem_text_headings(cid) to inspect available headings for a given CID.

Tests and quality

make test   # runs pytest
make lint   # ruff + mypy
make format # black + autofix lint

Documentation

Build and view the Sphinx docs locally:

pip install -e ".[docs]"
cd docs
make html  # or: python -m sphinx -b html . _build/html

Then open _build/html/index.html in your browser.

Publishing to GitHub Pages

A GitHub Actions workflow (.github/workflows/docs.yml) builds the Sphinx HTML docs on every push to main and publishes them to GitHub Pages.

One-time repo setup:

• In GitHub, go to Settings → Pages and set Source to GitHub Actions.

Manual trigger: use Actions → docs → Run workflow to publish immediately.

Publishing

This project uses Hatchling. To build and publish (requires valid PyPI credentials):

pip install hatch
hatch build
hatch publish

Notes

• Network access is required for live API calls to PubChem, ChEMBL, and UniChem.
• Protein embedding cache utilities expect torch if you use protein_embedding_cached; otherwise no heavy dependencies are required.