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datamol

Provide a Pythonic RDKit interface for molecular parsing, descriptors, and conformer generation.

1|Updated Mar 11, 2026
Search Tags:#visualization#cheminformatics#fingerprints#conformers#datamol#descriptors#rdkit
One-click install
npx skills add https://github.com/SciMate-AI/scicli --skill datamol-scimate-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datamol
Source: https://github.com/SciMate-AI/scicli/tree/main/internal/skills/bundled/claude-scientific-skills/skills/datamol
Command: npx skills add https://github.com/SciMate-AI/scicli --skill datamol-scimate-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Datamol provides a lightweight, Pythonic interface to RDKit, streamlining molecular data processing and cheminformatics workflows without requiring verbose RDKit boilerplate.

Core Features & Use Cases

  • Molecule creation and conversion: SMILES, InChI, SMARTS, SELFIES, and export/import cycles.
  • Standardization, sanitization, and cleanup: robust normalization and cleanup across datasets.
  • Descriptors and fingerprints: compute properties and fingerprints for similarity, docking, and ML.
  • 3D conformers and visualization: generate, analyze, and visualize 3D structures and conformational diversity.
  • Data I/O and pipelines: read/write common formats, batch processing, and cloud storage integration.

Quick Start

Install datamol and start by converting a SMILES string to a Mol, standardizing it, and computing basic descriptors.

Frequently Asked Questions about datamol

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

FAQPage Schema
How do I compute molecular descriptors and fingerprints for machine learning datasets?▼

Molecular descriptors and fingerprints for ML datasets can be computed using this RDKit wrapper to calculate properties and similarity vectors across small to medium datasets. It exposes Pythonic APIs for batch processing without verbose boilerplate.

What is the best way to standardize and sanitize molecule datasets in Python?▼

Standardizing and sanitizing molecule datasets in Python is handled through robust normalization and cleanup functions that process SMILES, InChI, and SELFIES inputs. This wrapper streamlines molecular data workflows by exposing RDKit operations under a single namespace.

Can I generate and visualize 3D conformers for molecular structures?▼

Generating and visualizing 3D conformers for molecular structures is fully supported, allowing you to analyze conformational diversity and render 3D structures. The interface wraps RDKit conformer generation to provide rapid visualization capabilities.

Does this cheminformatics tool support parallel processing for large datasets?▼

Parallel processing for large datasets is supported via the n_jobs parameter, enabling rapid molecular operations across small to medium datasets. It also integrates with fsspec for cloud storage to handle remote data workflows.

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

Converting between SMILES, InChI, and SELFIES formats is done through molecule creation and conversion APIs that handle import and export cycles. This wrapper provides a Pythonic interface for parsing string representations into molecular objects.

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