Chembl Database

The ChemBL database skill allows AI agents to query and retrieve structured chemical data, including compound properties, bioactivity results, and target information from a public repository. It enables automated discovery of drug-like molecules and their interactions with specific biological targets.

When to use it

• Analyzing the bioactivity profiles of known compounds against specific protein targets.
• Searching for novel chemical structures that match defined molecular property filters.
• Extracting standardized data on substance sources, synonyms, and canonical SMILES strings.
• Investigating relationships between chemicals and their associated diseases or pathways.

Key capabilities

• Querying the ChemBL database via SQL-like syntax or natural language requests.
• Retrieving detailed compound metadata including molecular weight, logP, and solubility.
• Accessing bioactivity data points such as IC50, Ki, and EC50 values from literature.
• Fetching target information including protein names, IDs, and organism sources.

Example prompts

• "Find all small molecules in the database that inhibit the EGFR kinase with an IC50 lower than 10 nM."
• "List the top 10 most studied compounds targeting the BRCA1 gene along with their primary data source."
• "Retrieve the canonical SMILES and InChIKey for aspirin and summarize its known adverse effects."

Tips & gotchas

Ensure your AI agent has internet access to reach the live ChemBL API, as this skill relies on external data retrieval. Results may vary based on the current version of the database; cite specific query parameters if reproducibility is critical for research validation.