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Chembl Drug

Community
beita6969
chembl-drug

Query the ChEMBL REST API for drug-target interactions, bioactivity data, ADMET properties, and approved drug information. Use when the user needs drug mechanism of action, binding affinity data, target information, or pharmacokinetic properties. NOT for basic compound lookup (use pubchem-compound), NOT for gene-disease associations (use open-targets), NOT for protein 3D structures (use pdb-structure).

Overview

Publisherbeita6969
RepositoryScienceClaw
Skill namechembl-drug
Stars
950
Forks
106
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Chembl Drug 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.
    https://github.com/beita6969/ScienceClaw/tree/main/skills/chembl-drug
  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/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/chembl-drug .claude/skills/chembl-drug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chembl Drug 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 Drug 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 Drug 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 Drug & Bioactivity Lookup

Query the ChEMBL REST API to access curated drug-target interaction data, bioactivity measurements, drug mechanisms of action, and ADMET properties from the European Bioinformatics Institute.

API Base URL

https://www.ebi.ac.uk/chembl/api/data

All endpoints accept .json suffix and return JSON by default. Use format=json as a query parameter alternatively.

API Endpoints

Molecule Lookup

Retrieve molecule details by ChEMBL ID or search by name:

bash
# Get molecule by ChEMBL ID
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.json" | head -80

# Search molecules by name
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/search.json?q=imatinib" | head -80

# Get molecule by canonical SMILES
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule.json?molecule_structures__canonical_smiles=CC(=O)Oc1ccccc1C(=O)O" | head -60

Target Lookup

Retrieve drug target information:

bash
# Get target by ChEMBL ID
curl -s "https://www.ebi.ac.uk/chembl/api/data/target/CHEMBL2034.json" | head -60

# Search targets by gene name
curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search.json?q=EGFR" | head -80

# Get target by UniProt accession
curl -s "https://www.ebi.ac.uk/chembl/api/data/target.json?target_components__accession=P00533" | head -60

Bioactivity Data

Retrieve binding affinity, IC50, Ki, and other activity measurements:

bash
# Get activities for a molecule (with pagination)
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity.json?molecule_chembl_id=CHEMBL25&limit=20" | head -100

# Get activities for a specific target
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id=CHEMBL2034&limit=20" | head -100

# Filter by activity type (IC50, Ki, Kd, EC50)
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity.json?molecule_chembl_id=CHEMBL941&standard_type=IC50&limit=10" | head -80

# Filter by potency threshold (pChEMBL value >= 6, i.e., activity <= 1 uM)
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id=CHEMBL2034&pchembl_value__gte=6&limit=20" | head -80

Drug Mechanisms of Action

bash
# Get mechanism of action for a drug
curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism.json?molecule_chembl_id=CHEMBL941" | head -60

# Get all mechanisms for a target
curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism.json?target_chembl_id=CHEMBL2034" | head -80

Approved Drugs

Filter for approved drugs and clinical candidates:

bash
# Get approved drugs only (max_phase = 4)
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule.json?max_phase=4&limit=20" | head -80

# Approved drugs for a specific target
curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism.json?target_chembl_id=CHEMBL2034" | head -60

# Filter by molecule type (small molecule, antibody, etc.)
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule.json?max_phase=4&molecule_type=Small%20molecule&limit=20" | head -60

ADMET and Drug Properties

bash
# Get computed molecular properties (Lipinski, PSA, ALogP are in molecule_properties)
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.json" | python3 -c "import sys,json; [print(f'{k}: {v}') for k,v in json.load(sys.stdin).get('molecule_properties',{}).items()]"

# Get drug indications
curl -s "https://www.ebi.ac.uk/chembl/api/data/drug_indication.json?molecule_chembl_id=CHEMBL941&limit=10" | head -60

Common Queries

bash
# Find all drugs targeting a specific protein
curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism.json?target_chembl_id=CHEMBL1862" | head -80

# Get the ChEMBL ID for a drug by name
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/search.json?q=metformin&limit=5" | head -40

# Get molecule image URL: https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL25.svg

Best Practices

  1. Always include limit parameter to control result size; default may return thousands of records.
  2. Use offset with limit for pagination through large result sets.
  3. Filter bioactivity by pchembl_value__gte=5 (10 uM) or pchembl_value__gte=6 (1 uM) for meaningful hits.
  4. Use max_phase to filter clinical status: 4 = approved, 3 = Phase III, 2 = Phase II, 1 = Phase I.
  5. Prefer ChEMBL IDs over name searches for precise lookups; name searches are fuzzy.
  6. Parse molecule_properties for pre-computed Lipinski descriptors, PSA, and ALogP.
  7. Rate limit to 1 request per second to avoid throttling from EBI servers.

Data Integrity Rule

NEVER fabricate database results from training data. Every protein ID, gene name, compound property, pathway ID, structure detail, and metadata MUST come from an actual API response in this conversation. If the API returns no results, errors, or partial data, report exactly what happened. Do not "fill in" missing data from memory or make up identifiers.

Frequently asked questions

What does the Chembl Drug AI skill do?

Query the ChEMBL REST API for drug-target interactions, bioactivity data, ADMET properties, and approved drug information. Use when the user needs drug mechanism of action, binding affinity data, target information, or pharmacokinetic properties. NOT for basic compound lookup (use pubchem-compound), NOT for gene-disease associations (use open-targets), NOT for protein 3D structures (use pdb-structure).

Why use Chembl Drug on TypingMind?

Because you install it once and use it with any model. Chembl Drug 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 Drug in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/chembl-drug. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Chembl Drug?

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 Drug?

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

Is the Chembl Drug AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT 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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