The rdkit-based molecular Python library

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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 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).

Documentation

Visit https://datamol-org--datamol.github.privpage.net/.

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.standardized_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")

Installation

Use conda:

mamba install -c conda-forge datamol

CI Status

Changelogs

See the latest changelogs at CHANGELOG.rst.

License

Under the Apache-2.0 license. See LICENSE.

Authors

See AUTHORS.rst.