Overview¶

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 rely on many options; Datamol provides good defaults by design.
🧠 Performance matters: built-in efficient parallelization when possible with 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).

Visit our website at https://datamol.io.

Updates¶

Datamol 0.13.0 refreshes the Python and RDKit stack, separates optional dependencies, and fixes molecule interchange and conformer handling. See the changelog and upgrade guide for the changes and upgrade details.

Installation¶

Add Datamol to a uv-managed project:

uv add datamol

Pip and conda-forge remain supported: python -m pip install datamol or 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.

`datamol`	`python`	`rdkit`
`0.13.x`	`[3.11, 3.12, 3.13, 3.14]`	`[2024.09, 2025.03, 2025.09, 2026.03]`
`0.12.x`	`[3.10, 3.11]`	`[2023.03, 2023.09]`
`0.11.x`	`[3.9, 3.10, 3.11]`	`[2022.09, 2023.03]`
`0.10.x`	`[3.9, 3.10, 3.11]`	`[2022.03, 2022.09]`
`0.9.x`	`[3.9, 3.10, 3.11]`	`[2022.03, 2022.09]`
`0.8.x`	`[3.8, 3.9, 3.10]`	`[2021.09, 2022.03, 2022.09]`
`0.7.x`	`[3.8, 3.9]`	`[2021.09, 2022.03]`
`0.6.x`	`[3.8, 3.9]`	`[2021.09]`
`0.5.x`	`[3.8, 3.9]`	`[2021.03, 2021.09]`
`0.4.x`	`[3.8, 3.9]`	`[2020.09, 2021.03]`
`0.3.x`	`[3.8, 3.9]`	`[2020.09, 2021.03]`