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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
  • ⚗ī¸ 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.
  • 🕹ī¸ Modern IO: out-of-the-box support for remote paths using fsspec to read and write multiple formats (sdf, xlsx, csv, etc).

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Use conda:

mamba install -c conda-forge datamol


You can replace mamba by conda.


We highly recommend using a Conda Python distribution to install Datamol. The package is also pip installable if you need it: pip install datamol.

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 =
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")


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

datamol python rdkit
0.3 >=3.7,<=3.9 >=2020.09,<=2021.03