Does this skill support cloud storage for chemical data?

Yes, Datamol supports modern I/O and can read or write molecular files (SDF, CSV, etc.) directly to S3, GCS, or via HTTP.

What is Datamol for Claude Code?

It is a skill that enables Claude to perform advanced molecular cheminformatics tasks by leveraging the Datamol library, which acts as a Pythonic wrapper around RDKit.

Can Datamol handle large molecular datasets?

Yes, Datamol includes built-in parallelization and batch processing capabilities to efficiently handle thousands of molecules simultaneously.

Is Datamol compatible with standard RDKit objects?

Absolutely. All molecular objects used and returned are native rdkit.Chem.Mol instances, ensuring full compatibility with the existing RDKit ecosystem.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: Microck

byMicrock

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Data Science & ML

Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic wrapper for RDKit.

About

Datamol is a specialized Claude Code Skill designed for researchers and developers working in drug discovery and molecular science. it provides a lightweight, high-level interface for RDKit, allowing users to perform complex molecular operations—such as structure standardization, 3D conformer generation, and fingerprint computation—with minimal boilerplate. By offering sensible defaults and built-in parallelization, Datamol streamlines the transition from raw SMILES strings to production-ready molecular datasets, making it an essential tool for chemical data science projects.

Key Features

Efficient scaffold analysis and molecular clustering
Batch computation of molecular descriptors and fingerprints
Standardized molecular format conversion (SMILES, SELFIES, InChI)
Advanced 3D conformer generation and energy minimization
Automated structure sanitization and chemical standardization
81 GitHub stars

Use Cases

Cleaning and preparing large molecular datasets for machine learning models
Analyzing chemical space diversity through clustering and scaffold grouping
Generating high-quality 3D molecular conformations for docking or simulation

About

Key Features

Efficient scaffold analysis and molecular clustering
Batch computation of molecular descriptors and fingerprints
Standardized molecular format conversion (SMILES, SELFIES, InChI)
Advanced 3D conformer generation and energy minimization
Automated structure sanitization and chemical standardization
81 GitHub stars

Use Cases

Cleaning and preparing large molecular datasets for machine learning models
Analyzing chemical space diversity through clustering and scaffold grouping
Generating high-quality 3D molecular conformations for docking or simulation