PatWalters/practical_cheminformatics_tutorials resource

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1300 stars · 228 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebook tutorials for learning practical cheminformatics using open-source tools like RDKit, datamol, and molfeat. The notebooks cover fundamentals such as SMILES/SMARTS, reaction enumeration, clustering, and working with databases like ChEMBL and BindingDB, and can be run on Google Colab or locally.

Use cases

• learn cheminformatics with RDKit
• tutorial on SMILES and SMARTS notation
• cluster molecules with k-means or Taylor-Butina
• work with ChEMBL drug data in Python
• enumerate reactions and stereoisomers
• run cheminformatics notebooks on Google Colab
• learn pandas for chemical data analysis

When to choose

• you want hands-on, runnable notebooks for learning cheminformatics from scratch
• you prefer open-source tools like RDKit and datamol without installing software (via Colab)
• you need practical examples of molecular clustering, reaction enumeration, or database analysis

When to avoid

• you need a production cheminformatics library or application rather than learning material
• you require comprehensive formal coursework or certification
• you need tutorials for proprietary cheminformatics software

Facets

learning-resource · maturity active

nlp data-science machine-learning developer-tools tutorials education data-science chemistry python cross-platform cheminformatics rdkit jupyter-notebooks drug-discovery smiles smarts molecular-clustering google-colab web

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