Chemistry Tutorials

Hands-on notebooks and practical guides for cheminformatics and computational chemistry.

Featured Tutorials

Practical Cheminformatics

github.com/PatWalters/practical_cheminformatics_tutorials

Patrick Walters — Jupyter & Colab notebooks covering clustering, SAR analysis, ML, and active learning.

TeachOpenCADD Talktorials

projects.volkamerlab.org/teachopencadd/talktorials.html

Volkamer Lab — Comprehensive “talktorials” (talk + tutorial) covering computer-aided drug design from basics to advanced topics.

Neural Networks for Chemists

github.com/omics101/acs-neural-networks

ACS In Focus (2024) — First-step knowledge for applying neural networks in chemistry research.

Tutorial Directory

Cheminformatics Basics

Tutorial	Topics	Format
Practical Cheminformatics	Fingerprints, clustering, SAR	Jupyter/Colab
TeachOpenCADD T001-T010	Molecular data, fingerprints, similarity	Jupyter
RDKit Cookbook	RDKit fundamentals	Documentation

Machine Learning for Chemistry

Tutorial	Topics	Format
Neural Networks for Chemists	NN fundamentals for chemistry	Jupyter
Transformers for Chemistry	LLMs, transformers	Jupyter
DeepChem Tutorials	GNNs, molecular property prediction	Jupyter

Reaction Prediction

Tutorial	Topics	Format
AI4Chemistry Course Labs	Reaction prediction, retrosynthesis	Jupyter

Running Tutorials

Google Colab: Most tutorials can be run directly in Google Colab without local setup. Look for the “Open in Colab” badge.

Local Setup


# Create a conda environment
conda create -n chem python=3.10
conda activate chem
 
# Install core dependencies
pip install rdkit jupyter pandas numpy scikit-learn
 
# For deep learning
pip install torch deepchem