datamol
A python library to work with molecules. Built on top of RDKit.
Decision gist · record as of 2026-08-14
Yes. Datamol is actively maintained, has no known vulnerabilities, and offers genuine convenience for RDKit-based workflows. Install it if you work with molecular structures and want a more ergonomic API than raw RDKit; the Apache-2.0 license poses no barrier. The main gotcha is rdkit's dependency chain—use conda-forge for a smooth install.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires rdkit, which is best installed via conda-forge; pip installation may require pre-built wheels or a working C++ compiler.
- Low install friction with a pure-wheel distribution.
- Actively maintained with recent commits and passing CI across Windows, OSX, and Linux.
License · maintenance · safety
Apache (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2024-06-10 (795 days) · last repo commit 2026-05-20 · 546 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 103,981 downloads/mo, #12,776 on PyPI
Alternatives
Verify before relying
import datamol as dm
mol = dm.to_mol("O=C(C)Oc1ccccc1C(=O)O", sanitize=True)
fp = dm.to_fp(mol)
smiles = dm.to_smiles(mol)- Whether rdkit can be reliably installed via pip in all environments, or if conda-forge is strongly recommended.
- Performance characteristics and scalability limits for large molecular datasets or batch operations.
What it is and what it does
Datamol is a Python library that wraps RDKit to simplify molecular cheminformatics workflows. It exposes RDKit's core Mol objects through a more intuitive API, handling common tasks like SMILES parsing, molecular fingerprinting, conformer generation, and format conversion with sensible defaults. The library emphasizes ease of use while maintaining direct access to underlying RDKit objects, so you can drop down to RDKit when needed.
It includes built-in parallelization via joblib, remote file support through fsspec for reading and writing SDF, CSV, and other formats from cloud storage, and visualization tools. The package targets chemists and computational biologists working with small-molecule datasets, offering both batch operations on DataFrames and single-molecule transformations.
Use it for
- Convert SMILES strings to molecular objects and compute fingerprints for machine learning pipelines.
- Standardize and sanitize molecular structures in bulk from CSV or SDF files stored locally or on cloud storage.
- Generate 3D conformers and compute solvent-accessible surface area for molecular dynamics or docking studies.
- Batch-process large molecular datasets with automatic parallelization and progress tracking.
- Visualize molecular structures and conformers in Jupyter notebooks with built-in 2D and 3D rendering.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Datamol is actively maintained, has no known vulnerabilities, and offers genuine convenience for RDKit-based workflows. Install it if you work with molecular structures and want a more ergonomic API than raw RDKit; the Apache-2.0 license poses no barrier. The main gotcha is rdkit's dependency chain—use conda-forge for a smooth install.
Install
datamol on PyPI
Before you install
Low install friction with a pure-wheel distribution. Actively maintained with recent commits and passing CI across Windows, OSX, and Linux. Requires rdkit as a dependency, which is typically installed via conda-forge; pip installation may require pre-built wheels.
Requires rdkit, which is best installed via conda-forge; pip installation may require pre-built wheels or a working C++ compiler.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
import datamol as dm
mol = dm.to_mol("O=C(C)Oc1ccccc1C(=O)O", sanitize=True)
fp = dm.to_fp(mol)
smiles = dm.to_smiles(mol)Verify before relying
- Whether rdkit can be reliably installed via pip in all environments, or if conda-forge is strongly recommended.
- Performance characteristics and scalability limits for large molecular datasets or batch operations.
Package facts
| License | Apache permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagestqdmlogurujoblibfsspecpandasnumpyscipymatplotlibpillowselfiesplatformdirsscikit-learnpackagingtyping-extensionsimportlib-resourcesrdkit |
| Maintenance | Actively maintained 795 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 103,981 / month, #12,776 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Bio-InformaticsTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Medical Science Apps. |
Evidence: datamol-0.12.5-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “rdkit wrapper library”
- datamolDatamol provides a pythonic layer on top of RDKit for molecular…
- rdkitRDKit is a chemoinformatics toolkit providing C++ and Python…
- pdbeccdutilsParse and process small molecule definitions from the wwPDB Chemical…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also rdkit · padelpy · mordredcommunity · chembl-structure-pipeline · pdbeccdutils · PubChemPy · epam-indigo · aimsim-core · py3Dmol · selfies