Can I perform structural searches with this skill?

Yes, the skill supports both similarity and substructure searches using SMILES strings to identify related chemical compounds.

Do I need to install any external libraries?

Yes, this skill requires the 'chembl_webresource_client' Python package to interface with the ChEMBL API.

Does the skill handle rate limiting and caching?

The ChEMBL Python client used by the skill includes automatic caching (defaulting to 24 hours) to optimize performance and respect fair usage policies.

Is it possible to export the data for further analysis?

Definitely. The skill provides patterns for converting query results into pandas DataFrames for advanced data analysis and visualization.

What is the primary use of the ChEMBL Database skill?

It allows developers and researchers to query medicinal chemistry data, including compound properties, bioactivity measurements, and drug targets, directly within an AI-assisted environment.

ChEMBL Database Explorer

Name: ChEMBL Database Explorer
Author: ricable

byricable

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データサイエンスとML

Accesses and queries the ChEMBL database for bioactive molecules, drug targets, and bioactivity data within medicinal chemistry workflows.

概要

The ChEMBL Database skill empowers AI agents to interact directly with the European Bioinformatics Institute's vast repository of bioactive molecules and drug discovery data. It provides programmatic access to over 2 million compounds, 19 million bioactivity measurements, and 13,000+ drug targets using the ChEMBL Python client. This skill is essential for researchers and developers performing compound searches, structure-activity relationship (SAR) studies, target-ligand interaction analysis, and virtual screening, enabling seamless integration of real-world medicinal chemistry data into AI-driven drug discovery pipelines.

主な機能

Retrieve molecule details and properties via ChEMBL ID or chemical name
Query bioactivity data including IC50, Ki, and EC50 measurements for specific targets
Access comprehensive drug information, including mechanisms of action and indications
1 GitHub stars
Perform chemical similarity and substructure searches using SMILES strings
Filter results using Django-style query operators for precise data extraction

ユースケース

Retrieving clinical data and mechanisms of action for drug repurposing research
Conducting structure-activity relationship (SAR) studies by analyzing similar molecular scaffolds
Identifying potent inhibitors and agonists for specific protein or enzyme targets

概要

主な機能

Retrieve molecule details and properties via ChEMBL ID or chemical name
Query bioactivity data including IC50, Ki, and EC50 measurements for specific targets
Access comprehensive drug information, including mechanisms of action and indications
1 GitHub stars
Perform chemical similarity and substructure searches using SMILES strings
Filter results using Django-style query operators for precise data extraction

ユースケース

Retrieving clinical data and mechanisms of action for drug repurposing research
Conducting structure-activity relationship (SAR) studies by analyzing similar molecular scaffolds
Identifying potent inhibitors and agonists for specific protein or enzyme targets