Datamol

What it does

This skill enables AI agents to interact with the Datamol library, a powerful toolkit for molecular data manipulation and analysis. It allows agents to process chemical structures, calculate properties, and perform cheminformatics tasks directly within their workflow.

When to use it

• Analyzing molecular fingerprints or descriptors for drug discovery projects.
• Converting between different molecular file formats like SMILES, SDF, or Mol2.
• Performing substructure searches or similarity calculations on large chemical datasets.
• Generating visualizations of 3D molecular conformations for reporting.

Key capabilities

• Loading and parsing various chemical file formats (e.g., SMILES, InChI, SDF).
• Calculating physicochemical properties such as LogP, molecular weight, and H-bond donors/acceptors.
• Performing substructure matching and similarity searches using Tanimoto coefficients.
• Generating 2D and 3D visualizations of molecular structures.
• Iterating over large datasets to filter or transform chemical libraries.

Example prompts

• "Load the SMILES string 'CCO' and calculate its molecular weight and LogP value."
• "Search this list of 100 molecules for any compounds containing a benzene ring substructure."
• "Convert these SDF file contents into a Datamol dataset and compute the average topological polar surface area."

Tips & gotchas

Ensure your AI agent has access to the datamol Python package installed in its environment before attempting to run molecular calculations. This skill is best used when working with chemical data pipelines that require rapid prototyping or analysis without writing extensive custom code.