Machine Learning in Chemistry: A Data Centred, Hands-on Introductory Machine Learning Course for Undergraduate Students
1. This innovative course, Machine Learning in Chemistry (MLChem), is designed specifically for undergraduate students, bridging the gap between traditional
RDKIT - PART 2
Describing Molecules - From Structure to Numbers
- Working with SMILES - The universal molecular language.
- Accessing Atomic and Bond Information - Iterating through a molecule.
- Molecular Properties & Descriptors (Part 1): Molecular Weight, LogP, and Counts.
PYTHON FOR RDKIT
PART 1
- The Foundation - Python & RDKit Basics
- Setting up your environment and saying "Hello, Molecule!
- The RDKit Molecule Object - The central data structure.
- Basic molecule visualization - Seeing what you're working with.
PYTHON IN CHEMINFORMATICS
PART 5
Capstone & Reflection
- Mini-project: choose a real-world cheminformatics problem (e.g., predict solubility, cluster a set of molecules, or build a virtual library).
Reflection: connecting your project to pharmaceutical applications.
PYTHON FOR CHEMINFORMATICS
PART 4
Advanced Applications
- QSAR modeling (Quantitative Structure–Activity Relationship)
- Docking workflows (intro level)
- Data curation and pipeline building
- Integration with machine learning (scikit-learn)
Python for Cheminformatics:
PART 1
Section A – Python Foundations (for scientific coding)
Basics: syntax, variables, data types
Control flow: if, for, while
Functions and modules
Data structures: lists, tuples, dictionaries, sets
File I/O (reading/writing)
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