Does this skill require any external Python packages?

Yes, it requires the 'chembl_webresource_client' which can be installed via pip or uv.

Is it possible to export ChEMBL data to a DataFrame?

Absolutely. The skill is designed to work with pandas, allowing you to convert query results into DataFrames for statistical analysis and visualization.

What is the ChEMBL Database skill for Claude Code?

It is a specialized plugin that allows Claude to query the ChEMBL database for chemical compounds, biological targets, and bioactivity data using the ChEMBL Python client.

What kind of bioactivity measurements can I retrieve?

You can retrieve various standard activity types including IC50, Ki, EC50, and pChEMBL values for millions of compound-target interactions.

Can I perform chemical structure searches with this skill?

Yes, the skill supports both similarity searches and substructure searches using SMILES strings.

ChEMBL Drug Discovery

Name: ChEMBL Drug Discovery
Author: jimmc414

byjimmc414

•

324

Data Science & ML

Queries the ChEMBL database for bioactive molecules, drug targets, and medicinal chemistry bioactivity data.

About

The ChEMBL Drug Discovery skill empowers researchers and developers to programmatically interact with one of the world's largest manually curated databases of bioactive molecules. By providing seamless access to over 2 million compounds and 19 million bioactivity measurements, this skill enables Claude to perform complex medicinal chemistry tasks such as target identification, IC50/Ki data retrieval, and chemical structure analysis. Whether you are conducting Structure-Activity Relationship (SAR) studies or identifying potential drug candidates, this skill bridges the gap between AI-driven reasoning and formal cheminformatics data retrieval.

Key Features

Access to drug mechanisms of action, clinical indications, and approval status
Advanced molecule and target queries using ChEMBL IDs or common names
Structure-based searching for chemical similarity and substructure matching
324 GitHub stars
Detailed bioactivity data retrieval including IC50, Ki, and EC50 measurements
Direct integration with pandas for seamless data analysis and export

Use Cases

Virtual screening of molecules based on Lipinski's Rule of Five and other properties
Analyzing Structure-Activity Relationships (SAR) by comparing similar compound potencies
Identifying potent inhibitors for specific protein targets during early-stage discovery

About

Key Features

Access to drug mechanisms of action, clinical indications, and approval status
Advanced molecule and target queries using ChEMBL IDs or common names
Structure-based searching for chemical similarity and substructure matching
324 GitHub stars
Detailed bioactivity data retrieval including IC50, Ki, and EC50 measurements
Direct integration with pandas for seamless data analysis and export

Use Cases

Virtual screening of molecules based on Lipinski's Rule of Five and other properties
Analyzing Structure-Activity Relationships (SAR) by comparing similar compound potencies
Identifying potent inhibitors for specific protein targets during early-stage discovery