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chembl-database

google-deepmind/science-skills · updated Jun 4, 2026

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$npx skills add https://github.com/google-deepmind/science-skills --skill chembl-database
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summary

### 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..."
skill.md
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

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. 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.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.
how to use chembl-database

How to use chembl-database on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/google-deepmind/science-skills --skill chembl-database

The skills CLI fetches chembl-database from GitHub repository google-deepmind/science-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/chembl-database

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

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.752 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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