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Chembl Database

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The ChEMBL database skill provides a structured interface to query the EBI ChEMBL database for information on bioactive molecules, drug targets, bioactivity data, and chemical structures. It utilizes a dedicated utility script to manage API interactions, ensuring rate limiting and error handling are managed correctly. The skill supports various operations including searching for molecules by name, fetching detailed target information, retrieving bioactivity data with unit normalization, and performing server-side structure-based searches like similarity and substructure matching.

What it does

  • Fetches detailed information for molecules, targets, and approved drugs using ChEMBL IDs.
  • Performs free-text searches for molecules, targets, and bioactivity data.
  • Retrieves and normalizes bioactivity data (e.g., IC50, Ki) to nM units for consistent analysis.
  • Executes server-side similarity and substructure searches using SMILES strings.
  • Downloads 2D chemical structure images in SVG or PNG formats.
  • Cross-references ChEMBL entities with external databases like UniProt.

When to use it

  • When you need to retrieve bioactivity data for specific drug targets or compounds.
  • When performing structure-based searches to find similar molecules or substructures.
  • When investigating drug mechanisms of action or clinical indications.
  • When you need to download chemical structure diagrams for research documentation.
  • When mapping protein targets to UniProt accessions or other cross-references.

Advantages

  • Ensures consistent API interaction through a centralized utility script.
  • Supports server-side structure searching without requiring local cheminformatics software.
  • Provides built-in unit normalization for bioactivity data.
  • Enables easy integration with other tools by outputting results to JSON files.
  • Facilitates batch processing and pagination for large datasets.

Usage examples

“Find the target information for EGFR.”
The agent uses the target search command to retrieve a list of EGFR-related targets and their corresponding ChEMBL IDs, saving the results to a JSON file.
“Get all IC50 values for CHEMBL203 normalized to nM.”
The agent queries the activity endpoint with filters for the target ID and standard type, applying the normalization flag to produce a file with consistent nM values.
“Find molecules similar to aspirin (SMILES: CC(=O)Oc1ccccc1C(=O)O) with 85% similarity.”
The agent executes a similarity search against the ChEMBL database and saves the matching molecules to a specified output file.
“Download a 2D structure image for CHEMBL25.”
The agent generates an SVG image file of the molecule structure for use in reports or presentations.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google-deepmind/chembl-database/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip chembl-database.zip -d .agents/skills

Documentation by Google DeepMind

ChEMBL Database Query

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

  2. User Notification: If .licenses/chembl_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://chembl.gitbook.io/chembl-interface-documentation/about, then (2) create the file recording the notification text and timestamp.

Core Rules

  • [!IMPORTANT] Use the Utility Scripts: You MUST ALWAYS use the provided utility script scripts/chembl_api.py for all ChEMBL API interactions, including checking status. NEVER use curl or custom Python requests to query the ChEMBL API directly. This ensures rate limit is enfoced and also retries on network errors.

  • Output to File (Required): The --output flag is required for every subcommand. All JSON results are written to the specified file. After running the command, read the output file with jq or your own code to extract the data. List results are typically wrapped in a JSON array keyed by the endpoint name (e.g., molecules, activities).

  • Notification: If this skill is used, ensure this is mentioned in the output.

Utility Script

All ChEMBL API queries use one script with subcommands:

uv run scripts/chembl_api.py <subcommand> --output <file> [options]

1. Check API Status
uv run scripts/chembl_api.py status --output /tmp/status.json

2. Molecule Queries

Fetch by ChEMBL ID: bash uv run scripts/chembl_api.py molecule --id CHEMBL25 --output /tmp/mol.json

Search by name: bash uv run scripts/chembl_api.py molecule --search "aspirin" --limit 3 --output /tmp/mol_search.json

Batch fetch: bash uv run scripts/chembl_api.py molecule --ids "CHEMBL25;CHEMBL1642" --limit 10 --output /tmp/mol_batch.json

Filter by properties: bash uv run scripts/chembl_api.py molecule --filter molecule_properties__mw_freebase__lte=500 --limit 5 --output /tmp/mol_filter.json

Filter by range: bash uv run scripts/chembl_api.py molecule --filter molecule_properties__mw_freebase__range=150,200 --limit 5 --output /tmp/mol_range.json

Download SDF structure file: bash uv run scripts/chembl_api.py molecule --id CHEMBL25 --dl_format sdf --output /tmp/aspirin.sdf

Tip: SDF/MOL files can be passed directly to tools like PyMOL or RDKit for 3D visualization and analysis.


3. Target Queries

Search for targets: bash uv run scripts/chembl_api.py target --search "EGFR" --limit 5 --output /tmp/targets.json

Fetch by ID: bash uv run scripts/chembl_api.py target --id CHEMBL203 --output /tmp/egfr.json


4. Bioactivity Data

Fetch activity by ID: bash uv run scripts/chembl_api.py activity --id 31863 --output /tmp/act.json

Search activities: bash uv run scripts/chembl_api.py activity --search "EGFR" --limit 5 --output /tmp/act_search.json

Filter activities for a target: bash uv run scripts/chembl_api.py activity --filter target_chembl_id=CHEMBL203 standard_type=IC50 --limit 10 --output /tmp/egfr_ic50.json

Normalize bioactivity units to nM: bash uv run scripts/chembl_api.py activity --filter target_chembl_id=CHEMBL203 standard_type=IC50 --limit 5 --normalize --output /tmp/egfr_normalized.json

Important: Bioactivity values come in various units (nM, µM, pM). Use --normalize to convert all values to nM for consistent comparison. Each record will include normalized_value_nM and normalization_note.


5. Drug Information

Fetch drug details: bash uv run scripts/chembl_api.py drug --id CHEMBL25 --output /tmp/drug.json

Drug indications: bash uv run scripts/chembl_api.py drug_indication --filter molecule_chembl_id=CHEMBL25 --limit 10 --output /tmp/indications.json

Filter indications by phase: bash uv run scripts/chembl_api.py drug_indication --filter molecule_chembl_id=CHEMBL25 max_phase_for_ind=4.0 --limit 10 --output /tmp/approved_indications.json

Drug warnings: bash uv run scripts/chembl_api.py drug_warning --limit 5 --output /tmp/warnings.json

Mechanisms of action: bash uv run scripts/chembl_api.py mechanism --filter molecule_chembl_id=CHEMBL25 --limit 5 --output /tmp/mech.json


6. Structure-Based Searches

Note: Both similarity and substructure searches are performed server-side on ChEMBL's pre-indexed database. They do not require a local RDKit installation.

Similarity search (SMILES + threshold): bash uv run scripts/chembl_api.py similarity --smiles "CC(=O)Oc1ccccc1C(=O)O" --similarity 85 --limit 5 --output /tmp/similar.json

Substructure search (SMILES): bash uv run scripts/chembl_api.py substructure --smiles "c1ccccc1" --limit 5 --output /tmp/substruct.json


7. Compound Image

Download a 2D structure image (SVG by default, scalable for publication):

uv run scripts/chembl_api.py image --id CHEMBL25 --output /tmp/chembl25.svg

Options:

  • --dimensions: Image size in pixels (max 500, default 500).

  • --engine: Rendering engine (default: rdkit).

  • --img_format: Output format — svg (default, vector) or png (raster).


8. Cross-Referencing with Other Databases

ChEMBL integrates with UniProt, Ensembl, PubChem, and other databases. Common cross-referencing patterns:

Find a ChEMBL target from a UniProt accession: bash uv run scripts/chembl_api.py target --filter target_components__accession=P00533 --limit 5 --output /tmp/uniprot_target.json

Resolve any ChEMBL ID to its entity type: bash uv run scripts/chembl_api.py chembl_id_lookup --id CHEMBL203 --output /tmp/lookup.json

Look up cross-reference sources: bash uv run scripts/chembl_api.py xref_source --limit 10 --output /tmp/xrefs.json

Tip: Use the target_component endpoint to find UniProt accessions, gene names, and protein sequences for any ChEMBL target.


9. Pagination

All list endpoints support --limit and --offset for pagination:

# First page: 2 results starting at offset 0
uv run scripts/chembl_api.py molecule --limit 2 --offset 0 --output /tmp/page1.json

# Second page: next 2 results starting at offset 2
uv run scripts/chembl_api.py molecule --limit 2 --offset 2 --output /tmp/page2.json

The response includes page_meta with total_count, limit, offset, next, and previous links. Use successive --offset values to page through large result sets.


10. Other Endpoints

All remaining endpoints follow the same pattern:

uv run scripts/chembl_api.py <subcommand> --output <file> [--id ID | --ids ID1;ID2 | --search QUERY] [--limit N] [--offset N] [--filter KEY=VAL ...]

Key subcommands at a glance:

  • molecule (searchable: true): Molecules/compounds — the primary entry point

  • target (searchable: true): Drug targets (proteins, organisms, etc.)

  • activity (searchable: true): Bioactivity data (IC50, Ki, EC50, etc.)

  • drug (searchable: false): Approved drugs

  • mechanism (searchable: false): Mechanisms of action

  • assay (searchable: true): Assay descriptions

  • similarity (searchable: false): Similarity search (special)

  • substructure (searchable: false): Substructure search (special)

  • image (searchable: false): Compound image download (special)

Full subcommand list:

  • activity_supp (searchable: false): Supplementary activity data

  • assay_class (searchable: false): Assay classifications

  • atc_class (searchable: false): ATC drug classifications

  • binding_site (searchable: false): Binding site information

  • biotherapeutic (searchable: false): Biotherapeutic molecules

  • cell_line (searchable: false): Cell line details

  • chembl_id_lookup (searchable: true): ChEMBL ID resolution

  • chembl_release (searchable: false): Database release info

  • compound_record (searchable: false): Compound records

  • compound_structural_alert (searchable: false): Structural alerts

  • document (searchable: true): Literature documents

  • document_similarity (searchable: false): Document similarity

  • drug_indication (searchable: false): Drug indications

  • drug_warning (searchable: false): Drug safety warnings

  • go_slim (searchable: false): GO slim terms

  • metabolism (searchable: false): Metabolism data

  • molecule_form (searchable: false): Molecule forms (salts/parents)

  • organism (searchable: false): Organisms

  • protein_classification (searchable: true): Protein classifications

  • source (searchable: false): Data sources

  • target_component (searchable: false): Target protein components

  • target_relation (searchable: false): Target relationships

  • tissue (searchable: false): Tissue types

  • xref_source (searchable: false): Cross-reference sources

  • status (searchable: false): API status check (special)

Common Options

  • --output FILE: Required. Output file path for JSON results.

  • --id ID: Fetch a single record by ID.

  • --ids ID1;ID2;...: Batch fetch multiple records.

  • --search QUERY: Free-text search (only for searchable endpoints, marked ✓).

  • --limit N: Max results to return (default: 5).

  • --offset N: Pagination offset.

  • --filter KEY=VAL: Filter parameters (can specify multiple).

  • --normalize: (activity only) Normalize values to nM.

  • --dl_format sdf|mol: (molecule only) Download structure file.

Reference

Workflow

  1. Use status --output /tmp/status.json to verify the API is available.

  2. Search for targets, molecules, or drugs using the relevant subcommand.

  3. Read the output JSON file to extract IDs and data.

  4. Use IDs from search results to fetch detailed records.

  5. Query activity with filters to get bioactivity data for targets/molecules. Use --normalize when comparing values across studies.

  6. Use similarity or substructure for server-side structure-based queries.

  7. Download compound images with image or structure files with molecule --dl_format sdf.

  8. Use target --filter target_components__accession=<UniProt> to cross- reference with UniProt.

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Google DeepMind
Publisher
Google DeepMind (Official)
Source
google-deepmind/science-skills · skills/chembl_database · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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