Applied Cheminformatics with RDKit

Modern chemistry is, in many ways, a data challenge. This training program translates chemical concepts into computer science terms, allowing you to leverage your existing expertise in string and graph manipulation to analyze, filter, and generate molecular structures.

Note: Session 1 is a free, 3-hour onboarding event. It is mandatory and open to anyone. The remaining six sessions (2-7) are paid and run for 2 hours each. There is no obligation to enroll in the paid program. You can decide whether to register for the full training program after the onboarding session.

What You’ll Learn

Session 1: Onboarding (Free)

• Review the program structure and logistics
• Install software and set up the environment
• Troubleshoot technical problems

Session 2: Molecular Representations and Depictions

• Learn how to represent molecules in 1D, 2D, and 3D
• Convert molecules between various formats
• Visualize molecules and generate image files
• Project: Develop a Chemical Converter to read molecular data from text files and save them as images

Session 3: Detecting Functional Groups with SMARTS

• Learn SMARTS patterns (Regular Expressions for Chemistry)
• Perform substructure searches
• Highlight matching patterns visually
• Project: Build a Functional Group Detector that scans a dataset and tags molecules containing specific features

Session 4: Converting Structures to Numerical Data

• Understand Molecular Descriptors (Representing chemistry as numbers)
• Compute key molecular properties
• Filter data using numerical thresholds and Lipinski’s Rule of Five
• Project: Build a Drug Candidate Filter that processes a CSV and extracts molecules suitable for further analysis

Session 5: Measuring Molecular Similarity

• Distinguish between substructure matching and similarity search
• Generate molecular fingerprints (hashing molecules into bit-vectors)
• Calculate similarity scores (Tanimoto, Dice)
• Project: Develop a Molecule Matcher tool that takes an input molecule and finds its top 5 matches in a database

Session 6: Clustering and Diversity Analysis

• Group similar molecules using Butina clustering
• Select diverse compounds using MaxMinPicker
• Visualize chemical space
• Project: Build a Diversity Picker that identifies the most unique structures in a large dataset

Session 7: Virtual Synthesis with Reaction SMARTS

• Define chemical transformations using Reaction SMARTS
• Run reactions and sanitize products
• Enumerate combinatorial libraries
• Project: Create a Library Generator that produces all possible products from a list of reactants