chembl-database▌
google-deepmind/science-skills · updated Jun 4, 2026
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### Chembl Database
- ›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..."
| 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. |
ChEMBL Database Query
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill 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.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
--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.svg
Options:
--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.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 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.
How to use chembl-database on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add chembl-database
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches chembl-database from GitHub repository google-deepmind/science-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate chembl-database. Access the skill through slash commands (e.g., /chembl-database) or your agent's skill management interface.
Security & Verification Notice
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.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★52 reviews- ★★★★★Nia Perez· Dec 24, 2024
Solid pick for teams standardizing on skills: chembl-database is focused, and the summary matches what you get after install.
- ★★★★★Ganesh Mohane· Dec 20, 2024
Solid pick for teams standardizing on skills: chembl-database is focused, and the summary matches what you get after install.
- ★★★★★Carlos Nasser· Dec 4, 2024
I recommend chembl-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Carlos Chen· Nov 23, 2024
chembl-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Neel Liu· Nov 15, 2024
We added chembl-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Rahul Santra· Nov 11, 2024
We added chembl-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Naina Choi· Nov 11, 2024
chembl-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Naina Park· Oct 14, 2024
Registry listing for chembl-database matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kwame Rao· Oct 6, 2024
chembl-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Pratham Ware· Oct 2, 2024
chembl-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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