RDKit Tutorials

A curated collection of Jupyter notebooks showcasing practical cheminformatics workflows with RDKit. The tutorials start with the fundamentals of reading and writing molecules and progress to advanced topics such as reaction enumeration, conformer analysis, and building QSAR models.

Table of Contents

• Prerequisites
• Environment Setup
• Running the Notebooks
• Tutorial Catalog
• Data
• Additional Resources

Prerequisites

• Anaconda or Miniconda
• Git (optional, but recommended if you plan to clone the repository)

Environment Setup

1. Clone or download the repository.

   git clone https://github.com/rdkit/rdkit_tutorials.git
   cd rdkit_tutorials

2. Create the Conda environment. The repository includes an environment specification at rdkit.yml.

   conda env create -f rdkit.yml

3. Verify the installation (optional).

   conda env list

4. Activate the tutorial environment.

   conda activate rdkit-training

5. Confirm the installed packages (optional).

   conda list

Running the Notebooks

With the rdkit-training environment active, launch Jupyter Notebook or JupyterLab:

jupyter notebook

or

jupyter lab

Open any of the notebooks below to explore the tutorials. Each notebook is self-contained and includes explanatory markdown cells and runnable code cells.

Tutorial Catalog

#	Notebook	Topic Overview
1	1_Reading and Writing Smiles using rdKit.ipynb	Read molecules from different formats, create SMILES, and save structures.
2	2_Property calculation, Drug-like filters, and Similarity maps.ipynb	Calculate physicochemical properties, apply drug-likeness filters, and render similarity maps.
3	3_Fingerprint Generation and Similarity Analysis.ipynb	Generate fingerprints and compute similarity metrics for compound collections.
4	4_Substructure and Similarity Search using rdKit.ipynb	Perform substructure and similarity searches across chemical libraries.
5	5_Conformer_Generation_and_3D_Analysis.ipynb	Embed 3D conformers, optimize geometries, and compare conformational ensembles.
6	6_Reaction_Enumeration_and_Scaffolds.ipynb	Enumerate products with reaction SMARTS and analyse Bemis–Murcko scaffolds.
7	7_Molecule_Standardization_and_Sanitization.ipynb	Clean molecules, neutralize charges, and extract parent fragments.
8	8_QSAR_Modeling_with_Scikit_Learn.ipynb	Build a toy QSAR classifier from Morgan fingerprints using scikit-learn.
9	9_Visualization_and_Drawing_Options.ipynb	Customise 2D depictions, grid images, and similarity maps.
10	10_Chemical_Format_Conversion_and_Metadata.ipynb	Convert between chemical formats and preserve metadata fields.

Data

Supporting data files referenced by the notebooks are located in the data/ directory. Keep the directory structure intact so that relative paths inside the notebooks continue to work.

Additional Resources

• RDKit Documentation
• RDKit Cookbook
• RDKit UGM Presentations for in-depth talks and slides.

Contributions are welcome! Open an issue or submit a pull request if you have ideas for improvements or spot any problems.