drugclaw/pharma-db-tools

Pharma DB Tools

Use this skill when the user asks for public drug-discovery database lookups rather than local cheminformatics analysis.

Typical triggers:

• compound lookup by name, CID, SMILES, or ChEMBL id
• public bioactivity or target-association lookup from ChEMBL
• measured drug-target affinity lookup from BindingDB by UniProt, compound, or local TSV export
• FDA labeling, adverse-event, NDC, approval, recall, or shortage data
• ClinicalTrials.gov study search, status review, or NCT lookup
• OpenAlex literature retrieval for drug, target, modality, or institution queries

Environment Check

The bundled template uses Python plus HTTP APIs. Check first.

which python3 || true
python3 - <<'PY'
mods = ["requests"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

If outbound network access is blocked, say so explicitly before claiming the lookup ran.

Bundled Asset

Use the reusable template instead of rewriting API snippets every time:

• templates/pharma_db_lookup.py

Supported sources:

• pubchem
• chembl
• bindingdb
• openfda
• clinicaltrials
• openalex

Quick Start

python3 templates/pharma_db_lookup.py pubchem \
  --query imatinib \
  --output pharma/pubchem_imatinib.csv \
  --summary pharma/pubchem_imatinib.json

python3 templates/pharma_db_lookup.py chembl \
  --mode molecule \
  --chembl-id CHEMBL941 \
  --output pharma/chembl_imatinib.csv \
  --summary pharma/chembl_imatinib.json

python3 templates/pharma_db_lookup.py bindingdb \
  --tsv BindingDB_All.tsv \
  --uniprot-id P00519 \
  --affinity-type Ki \
  --max-nm 1000 \
  --output pharma/bindingdb_abl1.csv \
  --summary pharma/bindingdb_abl1.json

python3 templates/pharma_db_lookup.py openfda \
  --endpoint label \
  --query imatinib \
  --output pharma/fda_imatinib_label.csv \
  --summary pharma/fda_imatinib_label.json

python3 templates/pharma_db_lookup.py clinicaltrials \
  --condition "non-small cell lung cancer" \
  --intervention osimertinib \
  --status RECRUITING \
  --output pharma/osimertinib_trials.csv \
  --summary pharma/osimertinib_trials.json

python3 templates/pharma_db_lookup.py openalex \
  --query "KRAS G12C inhibitor resistance" \
  --limit 20 \
  --output pharma/openalex_kras_g12c.csv \
  --summary pharma/openalex_kras_g12c.json

Working Rules

1. Save both a machine-readable result file and a summary JSON.
2. Report the exact database, mode, identifier, filters, and endpoint used.
3. Prefer exact identifiers when available: PubChem CID, ChEMBL id, NCT id, DOI.
4. Return direct stable links when the upstream database exposes them.
5. Distinguish clearly between compound metadata, activity measurements, regulatory evidence, clinical-study records, and literature hits.
6. If the API returns no hits, say that explicitly instead of inferring a scientific conclusion.
7. Treat these sources as evidence surfaces for prioritization and review, not as experimental proof.

Common Patterns

PubChem compound lookup by CID

python3 templates/pharma_db_lookup.py pubchem \
  --cid 5291 \
  --output pharma/pubchem_5291.csv \
  --summary pharma/pubchem_5291.json

ChEMBL activity rows for a target

python3 templates/pharma_db_lookup.py chembl \
  --mode activity \
  --target-id CHEMBL203 \
  --standard-type IC50 \
  --limit 25 \
  --output pharma/egfr_ic50.csv \
  --summary pharma/egfr_ic50.json

BindingDB measured affinities from a local export

python3 templates/pharma_db_lookup.py bindingdb \
  --tsv BindingDB_All.tsv \
  --compound-name imatinib \
  --affinity-type Ki \
  --limit 25 \
  --output pharma/imatinib_bindingdb.csv \
  --summary pharma/imatinib_bindingdb.json

openFDA adverse-event aggregation

python3 templates/pharma_db_lookup.py openfda \
  --endpoint event \
  --query pembrolizumab \
  --output pharma/pembro_events.csv \
  --summary pharma/pembro_events.json

ClinicalTrials.gov study detail

python3 templates/pharma_db_lookup.py clinicaltrials \
  --nct-id NCT04280705 \
  --output pharma/nct04280705.csv \
  --summary pharma/nct04280705.json

OpenAlex author- or institution-scoped literature

python3 templates/pharma_db_lookup.py openalex \
  --author "Jennifer Doudna" \
  --limit 20 \
  --output pharma/doudna_works.csv \
  --summary pharma/doudna_works.json

Output Expectations

Good answers should mention:

• which database and endpoint were queried
• the exact identifier or text query
• how many hits were returned
• the key IDs, names, phases, activities, warnings, affinity values, or citation counts
• the saved output paths
• any rate-limit, schema, or network caveats

Related Skills

For UniProt, PDB, AlphaFold, ClinVar, Ensembl, GEO, KEGG, Reactome, STRING, or OpenTargets, activate bio-db-tools. For DrugBank, ADMET, QSAR, descriptors, or structure-aware affinity work, activate chem-tools. For datamol, molfeat, PyTDC, or medchem-style library workflows, activate pharma-ml-tools. For docking, receptor preparation, or virtual screening execution, activate docking-tools.