datamol

datamol - molecular processing made easy

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.

`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]`

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.

	`main`
Lib build & Testing
Code Sanity (formatting and linting)
Documentation Build

License

Under the Apache-2.0 license. See LICENSE.

Metadata

Release files for datamol 0.13.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for datamol 0.13.0
File	Size	Uploaded
datamol-0.13.0.tar.gz	3.9 MB	Sep 9, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for datamol 0.13.0
File	Interpreter	ABI	Platform	Reset
datamol-0.13.0-py3-none-any.whl 497.8 kB Sep 9, 2026	Python 3	none	any	Details

Total release size: 4.4 MB

Release files / datamol-0.13.0.tar.gz

Download URL	datamol-0.13.0.tar.gz
Size	3.9 MB
Tags	Source
SHA-256 checksum How to use checksums	`8f226040eff6cd1ac6c8e48e1297b2c10a505ee4808ec926aa73ae9bde68df9b`
BLAKE2b-256 checksum How to use checksums	`2ce168636e590674a437aa975d59f465bafe0f06c0e890f70daa8c2fb71409d3`
Upload date	Sep 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`uv/0.12.8 {"installer":{"name":"uv","version":"0.12.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/datamol-io/datamol
Publishing commit: github.com/datamol-io/datamol/tree/37b7c9ba0a093df684f8bdb59ccbbf02a4955785

Workflow

Publishing configuration: github.com/datamol-io/datamol/blob/37b7c9ba0a093df684f8bdb59ccbbf02a4955785/.github/workflows/release.yml
Publishing logs: github.com/datamol-io/datamol/actions/runs/34358471109/attempts/1

Artifact

Subject: datamol-0.13.0.tar.gz
SHA-256 checksum: 8f226040eff6cd1ac6c8e48e1297b2c10a505ee4808ec926aa73ae9bde68df9b Verifying attestations

Release files / datamol-0.13.0-py3-none-any.whl

Download URL	datamol-0.13.0-py3-none-any.whl
Size	497.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`76ad6405aeabd698bd0d8e65d00bab03f751900e13eed38f836d1ab95583a958`
BLAKE2b-256 checksum How to use checksums	`3f4e6efcc76899d21149287749395ced6edc52b27b4399bcb6ecbb417a652143`
Upload date	Sep 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`uv/0.12.8 {"installer":{"name":"uv","version":"0.12.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/datamol-io/datamol
Publishing commit: github.com/datamol-io/datamol/tree/37b7c9ba0a093df684f8bdb59ccbbf02a4955785

Workflow

Publishing configuration: github.com/datamol-io/datamol/blob/37b7c9ba0a093df684f8bdb59ccbbf02a4955785/.github/workflows/release.yml
Publishing logs: github.com/datamol-io/datamol/actions/runs/34358471109/attempts/1

Artifact

Subject: datamol-0.13.0-py3-none-any.whl
SHA-256 checksum: 76ad6405aeabd698bd0d8e65d00bab03f751900e13eed38f836d1ab95583a958 Verifying attestations