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Chembl Database

OrganizationPopular
google-deepmind
chembl-database

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.

Overview

Publishergoogle-deepmind
Repositoryscience-skills
Skill namechembl-database
Stars
3.1K
Forks
349
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by google-deepmind on GitHub. Read the source before you install it.

Installation

Install the Chembl Database AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google-deepmind/science-skills.git /tmp/science-skills
mkdir -p .claude/skills
cp -r /tmp/science-skills/skills/chembl_database .claude/skills/chembl-database
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chembl Database in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Chembl Database on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Chembl Database is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

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

1. Check API Status

bash
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):

bash
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:

bash
# 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:

bash
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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Chembl Database AI skill do?

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.

Why use Chembl Database on TypingMind?

Because you install it once and use it with any model. Chembl Database is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Chembl Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google-deepmind/science-skills/tree/main/skills/chembl_database. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Chembl Database?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Chembl Database?

As many as you like. As long as a model supports skills, you can use Chembl Database with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Chembl Database AI skill free?

Yes. It is published on GitHub by google-deepmind under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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