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Digital Chemistry
Last Updated: 2026-06-01 11:33:01
Abstract
This course introduces the topic of digital chemistry, an emerging interdisciplinary field that intersects machine learning, cheminformatics, computational chemistry and software development. In contrast to specialized courses on these separate topics, this course focuses on how to combine the best tools and approaches of each field to solve chemical problems.
Objective
Students will gain fundamental knowledge of digital chemistry and will be well-prepared to engage with subject specialists in academia or industry. They will know how to work with and visualize chemical data and train predictive and generative machine learning models. Students will learn how to apply critical thinking and sound skepticism to build well-grounded and validated models. By completing a hands-on project, students will get practical experience of leading a machine learning project from start to finish.
Content
- Introduction to Python for chemists - Practical cheminformatics - Introduction to data curation, extraction, and management - Generating representations and simulated data with computational chemistry methods - Classical supervised and unsupervised machine learning - Advanced machine learning: neural networks, graph models, language models - Explainable AI - Applications of digital chemistry to property and reaction prediction, molecular design and experimental design
Resources
Lecture Notes
Lecture notes will be provided.
Literature
The course is based on the lectures notes, and accompanying code notebooks, but the following books can be used as reference: - K. A. Tanemura, D. Sierra-Costa, K. M. Merz Jr., Python for Chemists - J. P. Janet, H. J. Kulik, Machine Learning in Chemistry - G. M. Jones, B. Story, V. Maroulas, K. D. Vogiatzis, Molecular Representations for Machine Learning - S. Raschka, Y. Liu, V. Mirjalili, Machine Learning with PyTorch and Scikit-Learn - A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition - A. D. White, Deep Learning for Molecules & Materials - G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning - T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning - Y. S. Abu-Mostafa, M. Magdon-Ismail, H.-T. Lin, Learning From Data
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 30 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Digital Chemistry |
|
3 h weekly |
Offered In
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Wahlfächer (Den Studierenden stehen der Studienstufe angemessene chemische Lehrangebote des D-CHAB zur Auswahl offen (Zulassungsbedingungen beachten).)
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