datamol - molecular processing made easy

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Datamol is a python library to work with molecules. It's a layer built on top of RDKit and aims to be as light as possible.

• 🐍 Simple pythonic API
• ⚗️ RDKit first: all you manipulate are rdkit.Chem.Mol objects.
• ✅ Manipulating molecules often relies on many options; Datamol provides good defaults by design.
• 🧠 Performance matters: built-in efficient parallelization when possible with an optional progress bar.
• 🕹️ Modern IO: out-of-the-box support for remote paths using fsspec to read and write multiple formats (sdf, xlsx, csv, etc).

Try Online

Visit and try Datamol online.

Documentation

Visit https://docs.datamol.io.

Updates

Datamol 0.13.0 modernizes the supported scientific Python stack while keeping the library focused on reliable molecular processing. It adds RDKit binary and property-dictionary interchange, enforces conformer RMS cutoffs with symmetry-aware alignment, fixes reported RDKit compatibility issues, and separates optional I/O and visualization dependencies from the core installation.

See the complete changelog for all notable changes and the 0.13.0 upgrade guide for compatibility and transition details.

Installation

Add Datamol to a uv-managed project:

uv add datamol

Pip and conda-forge remain supported:

python -m pip install datamol
mamba install -c conda-forge datamol

Datamol 0.13.0 requires Python 3.11 or newer and RDKit 2024.09 or newer. Cloud I/O (S3, Google Cloud), Excel/Parquet, visualization and SELFIES support are all included by default — there are no feature extras to install.

Quick API Tour

import datamol as dm

# Common functions
mol = dm.to_mol("O=C(C)Oc1ccccc1C(=O)O", sanitize=True)
fp = dm.to_fp(mol)
selfies = dm.to_selfies(mol)
inchi = dm.to_inchi(mol)

# Standardize and sanitize
mol = dm.to_mol("O=C(C)Oc1ccccc1C(=O)O")
mol = dm.fix_mol(mol)
mol = dm.sanitize_mol(mol)
mol = dm.standardize_mol(mol)

# Dataframe manipulation
df = dm.data.freesolv()
mols = dm.from_df(df)

# 2D viz
legends = [dm.to_smiles(mol) for mol in mols[:10]]
dm.viz.to_image(mols[:10], legends=legends)

# Generate conformers
smiles = "O=C(C)Oc1ccccc1C(=O)O"
mol = dm.to_mol(smiles)
mol_with_conformers = dm.conformers.generate(mol)

# 3D viz (using nglview)
dm.viz.conformers(mol, n_confs=10)

# Compute SASA from conformers
sasa = dm.conformers.sasa(mol_with_conformers)

# Easy IO
mols = dm.read_sdf("s3://my-awesome-data-lake/smiles.sdf", as_df=False)
dm.to_sdf(mols, "gs://data-bucket/smiles.sdf")

How to cite

Please cite Datamol if you use it in your research: .

Compatibilities

Version compatibilities are an essential topic for production-software stacks. We are cautious about documenting compatibility between datamol, python and rdkit.

See below the associated versions of Python and RDKit, for which a minor version of Datamol has been tested during its whole lifecycle. It does not mean other combinations does not work but that those are not tested.

CI Status

The CI runs tests and performs code quality checks for the following combinations:

• Linux x86-64, Windows x86-64, macOS Apple Silicon and macOS Intel.
• Python 3.11 through 3.14 on Linux.
• The supported RDKit release series from 2024.09 through 2026.03.
• The latest supported stack on every operating-system and architecture lane.

License

Under the Apache-2.0 license. See LICENSE.