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Molecules & Models
Foundations
Cheminformatics
Bioinformatics
Generative
Appendices
Part I: Foundations
0
Start here
0.1
How this course works
15 min
0.2
Setting up Python
30 min
0.3
Python self-check
20 min
Chapter 0 key points
1
Scientific Python for data work
1.1
Python refresher for scientists
40 min
1.2
NumPy arrays
55 min
1.3
pandas DataFrames
65 min
1.4
Visualising data with matplotlib and seaborn
50 min
1.5
EDA case study: aqueous solubility
55 min
Lab
Guided EDA: the breast cancer Wisconsin dataset
60–90 min
Chapter 1 key points
2
Machine learning fundamentals
2.1
What is machine learning?
35 min
2.2
Linear regression from scratch, then scikit-learn
60 min
2.3
Evaluating models honestly
55 min
2.4
Overfitting, regularisation and cross-validation
70 min
2.5
Classification
65 min
2.6
Pipelines, preprocessing and data leakage
65 min
Lab
First end-to-end ML project: predicting solubility
90 min
Quiz
Chapter checkpoint
Chapter 2 key points
3
Beyond linear models
soon
4
Deep learning essentials with PyTorch
soon
Part II: Cheminformatics
5
Molecules as data
soon
6
QSAR and property prediction
soon
7
Graph neural networks for molecules
soon
8
Structure-based design
soon
Part III: Bioinformatics
9
Biological sequences and databases
soon
10
Machine learning on sequences
soon
11
Omics data
soon
12
Protein structure
soon
Part IV: Generative
13
Generative models for molecules
soon
14
Generative design loops
soon
15
Capstone projects
soon
Appendices
A. Maths refresher
B. Glossary
C. Reproducibility toolkit
soon
D. Further reading
soon
Resources
0.2
Setting up Python
30 min
Glossary
Maths refresher
Chapter key points
Appendices
A · Maths refresher
B · Glossary
C · Reproducibility toolkit
(coming soon)
D · Further reading
(coming soon)