Drug Db Chembl

db-chembl

Goal

To programmatically query the ChEMBL database web services and retrieve reproducible, model-ready datasets of targets, molecules, and bioactivities, while preserving provenance (assay/document IDs) and enabling common curation filters (e.g., pChEMBL, standardized units, handling censoring operators, assay type).

ChEMBL activity data is curated and standardized, but downstream modeling still requires careful selection/filters to avoid mixing incompatible assay formats or censored measurements.

Instructions

1. Search for candidate targets by name (broad recall)

Use this when you only have a gene/protein string and want candidate ChEMBL target IDs.

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --target "EGFR" \
  --max_results 20 \
  --output egfr_targets.json

2. Resolve target by UniProt accession (higher precision)

If you know a UniProt accession, this reduces ambiguity compared to free-text searching.

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --uniprot "P00533" \
  --target_type "SINGLE PROTEIN" \
  --max_results 10 \
  --output egfr_targets_uniprot.json

3. Retrieve bioactivity data for a target (recommended "model-ready" defaults)

ChEMBL web services are paginated (limit/offset + page_meta); this script automatically iterates pages up to --max_results.

Recommended for many QSAR/ML use cases:

• use standardized fields (standard_*)
• prefer binding assays (--assay_type B) when you want binding potency
• restrict to equality relations (--standard_relation "=") to avoid mixing censored labels
• restrict to nM for consistency (--standard_units nM)
• require/compute pChEMBL (comparable negative log molar potency)

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --target_id "CHEMBL203" \
  --activity_type "IC50" \
  --assay_type "B" \
  --standard_relation "=" \
  --standard_units "nM" \
  --require_pchembl \
  --pchembl_min 5.0 \
  --max_results 200 \
  --output egfr_ic50_pchembl.json

4. Look up a molecule record (ChEMBL ID or InChIKey)

Prefer ChEMBL ID or InChIKey for exact identity.

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --chembl_id "CHEMBL25" \
  --output aspirin_record.json

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --inchi_key "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" \
  --output aspirin_record_by_inchikey.json

5. Chemical search by SMILES (similarity or substructure)

SMILES strings often differ by canonicalization; similarity/substructure search is usually more robust than "exact SMILES match."

Similarity search (default cutoff 70):

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --smiles_mode similarity \
  --similarity 80 \
  --max_results 10 \
  --output aspirin_similarity.json

Substructure search:

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --smiles_mode substructure \
  --max_results 10 \
  --output aspirin_substructure.json

6. Export as CSV for quick inspection

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --target_id "CHEMBL203" \
  --activity_type "IC50" \
  --assay_type "B" \
  --standard_relation "=" \
  --standard_units "nM" \
  --require_pchembl \
  --max_results 200 \
  --output egfr_ic50_pchembl.csv

Examples

EGFR binding-potency dataset (IC50) with comparable pChEMBL values:

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py --target "EGFR" --max_results 10 --output egfr_targets.json

# Env: base-agent
python .agents/skills/drug-db-chembl/scripts/query_chembl.py \
  --target_id "CHEMBL203" \
  --activity_type "IC50" \
  --assay_type "B" \
  --standard_relation "=" \
  --standard_units "nM" \
  --require_pchembl \
  --pchembl_min 5.0 \
  --max_results 200 \
  --output egfr_ic50_pchembl.json

Constraints

• Pagination: ChEMBL web services return results in pages (limit/offset) with page_meta. Use --max_results to cap downloads.
• Rate limiting / etiquette: This script inserts a small delay between requests; you can adjust it with --delay.
• Data comparability:
• Prefer standardized fields (standard_type/value/units/relation) and pChEMBL for comparable potency where appropriate.
• Do not mix censored relations (>, <) into regression labels unless you explicitly model censoring.
• Target mapping confidence: ChEMBL assigns a 0-9 confidence score to assay-to-target mappings; consider using it when building high-precision datasets.
• Environment: Requires base-agent conda environment.
• Dependencies: Standard library only (urllib, json, csv, etc.).

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Author: Matthew Cox Contact: GitHub @mcox3406

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: learningmatter-mit
• Source: learningmatter-mit/AtomisticSkills
• License: MIT
• Homepage: https://arxiv.org/abs/2605.24002

Install and usage instructions live in the source repository linked above.