### Chembl Database
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name: "chembl-database"
description: "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 mechan..."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionchembl-databaseExecute the skills CLI command in your project's root directory to begin installation:
Fetches chembl-database from google-deepmind/science-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate chembl-database. Access via /chembl-database in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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| name | chembl-database |
| description | > 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. |
uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH.[!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.
All ChEMBL API queries use one script with subcommands:
uv run scripts/chembl_api.py <subcommand> --output <file> [options]
uv run scripts/chembl_api.py status --output /tmp/status.json
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.
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
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.
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
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
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).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.
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.
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)--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.status --output /tmp/status.json to verify the API is available.activity with filters to get bioactivity data for targets/molecules.
Use --normalize when comparing values across studies.similarity or substructure for server-side structure-based queries.image or structure files with molecule --dl_format sdf.target --filter target_components__accession=<UniProt> to cross-
reference with UniProt.Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
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✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
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Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Solid pick for teams standardizing on skills: chembl-database is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: chembl-database is focused, and the summary matches what you get after install.
I recommend chembl-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
chembl-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added chembl-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added chembl-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
chembl-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for chembl-database matched our evaluation — installs cleanly and behaves as described in the markdown.
chembl-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
chembl-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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