Machine Learning and Molecules (MLmol)

Course content

Students write their own neural network code from scratch using Python and use it to predict chemical properties of molecules. The performance is compared to standard machine learning packages such as scikit-learn, Keras, and DeepChem.


Learning outcome

Basic principles behind Python programming, machine learning, and cheminformatics. Classification and regression using neural networks. Activation functions, back propagation using gradient descent, Overfitting, regularisation, hyperparameter optimisation, and training/validation/test sets.  SMILES strings, molecular fingerprints, and graph convolution as applied to molecules.

Data manipulation and visualisation using Pandas, numpy, and Matplotlib/Seaborn. Manipulation of chemical data using RDKit. Use of scikit-learn, Keras, and DeepChem. 

Prediction of chemical properties using machine learning. Critical evaluation of machine learning models.

Videolectures and classroom discussion

See Absalon for a list of course literature

First year organic chemistry and mathematics

Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
7,5 ECTS
Type of assessment
Oral examination, 30 minutes
Type of assessment details
A final 30-minute individual oral examination is without preparation and based on the project report. The exam begins with a 5 minute presentation from the examinee, after which the internal assessors ask questions in the project area.
Exam registration requirements

All three assignments have to be approved by the end of the 7th week; b) the exam report has to be submitted by the end of the 8th week of the block

All aids allowed

The use of Large Language Models (LLM)/Large Multimodal Models (LMM) – such as ChatGPT and GPT-4 – is permitted.

Marking scale
passed/not passed
Censorship form
No external censorship
Several internal examiners

Same as ordinary exam. The same report, possibly improved based on feedback, can be used for the oral re-exam.

Criteria for exam assessment

See Learning Outcome

  • Category
  • Hours
  • Class Instruction
  • 12
  • Preparation
  • 93,5
  • E-Learning
  • 50
  • Project work
  • 50
  • Exam
  • 0,5
  • English
  • 206,0


Course number
7,5 ECTS
Programme level

1 block

Block 2, Block 3 And Block 4
Much of the instruction comes from video lectures supplemented by one weekly meeting for Q&A.
The number of seats may be reduced in the late registration period
Study Board of Physics, Chemistry and Nanoscience
Contracting department
  • Department of Chemistry
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Jan Halborg Jensen   (8-6e6c6e697277697244676c6971326f7932686f)
Saved on the 24-08-2023

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