# Datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults.

- Repository: https://github.com/K-Dense-AI/scientific-agent-skills (55651 stars)
- Category: Media
- License: Apache-2.0 license
- Last updated: 2026-10-26
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add K-Dense-AI/scientific-agent-skills --skill datamol -g -a claude-code -y`
Codex: `npx skills add K-Dense-AI/scientific-agent-skills --skill datamol -g -a codex -y`
Cursor: `npx skills add K-Dense-AI/scientific-agent-skills --skill datamol -g -a cursor -y`
Gemini CLI: `npx skills add K-Dense-AI/scientific-agent-skills --skill datamol -g -a gemini-cli -y`

## Description

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Datamol Cheminformatics Skill

## Overview

Datamol is a Python library that provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. Simplify complex molecular operations with sensible defaults, efficient parallelization, and modern I/O capabilities. All molecular objects are native `rdkit.Chem.Mol` instances, ensuring full compatibility with the RDKit ecosystem.

**Verified target:** datamol **0.13.0**, released September 9, 2026. Local checks used
Python 3.13 and RDKit 2026.03.6. Python 3.11+ and RDKit 2024.09+ are required.
The 0.13 distribution includes SELFIES, visualization, cloud, Excel and Parquet
runtime dependencies. Current API examples and verification limits are recorded in
[references/review.md](references/review.md). Pin the environment: RDKit upgrades can
change canonical representations and retained conformers. Compare chemical invariants,
not a fixed conformer count or an exact version-dependent canonical string.

**Key capabilities**:
- 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

## Installation and Setup

Guide users to install datamol:

```bash
uv pip install "datamol==0.13.0"
```

RDKit and the S3/GCS, Excel/Parquet, visualization and SELFIES dependencies are
installed by the current distribution; separate feature extras are not required.
Use an isolated environment to avoid conflicting scientific-package requirements.

**Import convention**:
```python
import datamol as dm
```

## Core Workflows

Ten workflow areas, each with worked code, are documented in
[references/core_workflows.md](references/core_workflows.md):

The rest: https://github.com/K-Dense-AI/scientific-agent-skills/blob/HEAD/skills/datamol/SKILL.md