A set of Jupyter notebooks for learning Cheminformatics. The links below will open the tutorials on Google Colab. This way you can run the notebooks without having to install software on your computer. Of course, you can also just clone the repo and run these notebooks on your own computer.

1. A Whirlwind Introduction to the RDKit for Cheminformatics
2. A Brief Introduction to Pandas for Cheminformatics
3. SMILES Tutorial
4. SMARTS Tutorial
5. Recursive SMARTS Tutorial
6. Reaction Enumeration, the basics
7. Reaction Enumeration, advanced
8. Enumerating Stereoisomers and Tautomers

9. Data Manipulation, Descriptors and Clustering

10. K-Means Clustering
11. Taylor-Butina Clustering
12. Self-Organizing Maps

13. ChEMBL Ring System Analysis
14. Working With Drug Data from the ChEMBL Database

15. Identifying Scaffolds
16. R-group Analysis
17. Positional Analogue Scanning
18. Free-Wilson Analysis
19. Matched Molecular Pairs
20. Matched Molecular Series

21. Build and Test a QSAR Model in 8 Lines of Python
22. Building a Classification Model
23. Comparing Classification Models
24. Building a Regression Model
25. Comparing Regression Models

26. Active Classification
27. Active Regression
28. Active Shape Search

29. Tautomer Energies with Auto3D

These notebooks began as part of a two-day workshop I did at the University of Bonn in 2019. Since then, they have evolved, and additional notebooks have been added. Many of the notebooks started as posts on my blog, Practical Cheminformatics. I originally had everything running on Binder, but I found some capacity limits with larger groups and moved everything to Google Colab. I plan to continue adding to and revising these as long as people are interested.

This is a work in progress. As you can see above, I'm still working on a few notebooks. It's more than possible that these tutorials contain mistakes and/or typos. If you find something that should be corrected, please submit an issue or a PR. In addition, I'm always looking to improve the text. Please let me know if aspects could be explained more clearly. I'd also like to hear more about topics you'd like to see covered. If you find these tutorials useful, please consider starring this repo.

These tutorials wouldn't be possible without the work of Greg Landrum, Brian Kelley, and the RDKit team, as well as Cédric Bouysset and his work on mols2grid. Thanks to Noel O'Boyle, Rafael Lameiro, and Jennifer HY Lin for helping to keep the notebooks up to date as the underlying software libraries evolve.