datamol

Wrap RDKit in a Pythonic interface for cheminformatics workflows.

1|2|Updated Apr 29, 2026

Search Tags:#cheminformatics #io #parallel-processing #fingerprints #conformers #descriptors #rdkit

Ready to run

npx skills add https://github.com/fuzzy-dynamics/strings --skill datamol-fuzzy-dynamics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: datamol
Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/datamol
Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill datamol-fuzzy-dynamics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Datamol provides a lightweight Pythonic abstraction layer over RDKit to simplify molecular cheminformatics tasks and reduce boilerplate code.

Core Features & Use Cases

Molecular format conversion (SMILES, SELFIES, InChI)
Structure standardization and sanitization
Molecular descriptors and fingerprints
3D conformer generation and analysis
Clustering and diversity selection
Scaffold and fragment analysis
Chemical reaction application
Visualization and alignment
Batch processing with parallelization
Cloud storage support via fsspec

Quick Start

Install datamol, import it as dm, and load a molecule to start working.

Frequently Asked Questions about datamol

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calculate molecular descriptors and fingerprints in RDKit without writing boilerplate code?▼

You can calculate molecular descriptors and fingerprints by using a high-level Pythonic wrapper around RDKit, which streamlines cheminformatics workflows and returns native RDKit Mol objects without boilerplate code.

What's the best way to standardize and sanitize molecular structures in Python?▼

The best way to standardize and sanitize molecular structures is using a Pythonic RDKit wrapper that provides a consistent API surface for structure standardization and sanitization across molecule data.

Can I generate 3D conformers and perform clustering analysis on large molecule datasets?▼

Yes, you can generate 3D conformers and perform clustering analysis on large molecule datasets using a Pythonic RDKit interface that supports batch processing with parallelization and cloud storage I/O.

How do I convert between SMILES, SELFIES, and InChI molecular formats?▼

You can convert between SMILES, SELFIES, and InChI molecular formats through a lightweight Pythonic abstraction layer over RDKit that simplifies molecular format conversion tasks and reduces boilerplate code.

Does this cheminformatics tool support cloud storage and parallel processing for batch molecule operations?▼

Yes, this cheminformatics tool supports cloud storage via fsspec and parallel processing, enabling efficient batch molecule operations and scalable file I/O across large datasets.

Why use a Pythonic wrapper over native RDKit for cheminformatics workflows?▼

A Pythonic wrapper reduces boilerplate code and provides a consistent API surface for cheminformatics workflows, streamlining complex RDKit operations like fragmentation, conformer analysis, and visualization into a high-level interface.