Chembl Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/chembl-database/downloadUnzip 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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.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.pyfor all ChEMBL API interactions, including checking status. NEVER usecurlor 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
--outputflag 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.json2. 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
--normalizeto convert all values to nM for consistent comparison. Each record will includenormalized_value_nMandnormalization_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.svgOptions:
--dimensions: Image size in pixels (max 500, default 500).--engine: Rendering engine (default: rdkit).--img_format: Output format —svg(default, vector) orpng(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_componentendpoint 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.jsonThe 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 pointtarget(searchable: true): Drug targets (proteins, organisms, etc.)activity(searchable: true): Bioactivity data (IC50, Ki, EC50, etc.)drug(searchable: false): Approved drugsmechanism(searchable: false): Mechanisms of actionassay(searchable: true): Assay descriptionssimilarity(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 dataassay_class(searchable: false): Assay classificationsatc_class(searchable: false): ATC drug classificationsbinding_site(searchable: false): Binding site informationbiotherapeutic(searchable: false): Biotherapeutic moleculescell_line(searchable: false): Cell line detailschembl_id_lookup(searchable: true): ChEMBL ID resolutionchembl_release(searchable: false): Database release infocompound_record(searchable: false): Compound recordscompound_structural_alert(searchable: false): Structural alertsdocument(searchable: true): Literature documentsdocument_similarity(searchable: false): Document similaritydrug_indication(searchable: false): Drug indicationsdrug_warning(searchable: false): Drug safety warningsgo_slim(searchable: false): GO slim termsmetabolism(searchable: false): Metabolism datamolecule_form(searchable: false): Molecule forms (salts/parents)organism(searchable: false): Organismsprotein_classification(searchable: true): Protein classificationssource(searchable: false): Data sourcestarget_component(searchable: false): Target protein componentstarget_relation(searchable: false): Target relationshipstissue(searchable: false): Tissue typesxref_source(searchable: false): Cross-reference sourcesstatus(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
API Endpoints Reference: See references/api_endpoints.md for the full list of endpoints and filter operators.
Workflow
Use
status --output /tmp/status.jsonto verify the API is available.Search for targets, molecules, or drugs using the relevant subcommand.
Read the output JSON file to extract IDs and data.
Use IDs from search results to fetch detailed records.
Query
activitywith filters to get bioactivity data for targets/molecules. Use--normalizewhen comparing values across studies.Use
similarityorsubstructurefor server-side structure-based queries.Download compound images with
imageor structure files withmolecule --dl_format sdf.Use
target --filter target_components__accession=<UniProt>to cross- reference with UniProt.
Files
- ATTRIBUTION.mdAdded by AutoGineer912 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md11.2 KiB
- references/api_endpoints.md2.8 KiB
- references/citation.bib3.6 KiB
- scripts/chembl_api.pyScript18.4 KiB
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
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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)
- Synced
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download chembl-database.zip