NKEB26002U Machine Learning and Molecules (MLmol)

Volume 2026/2027
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

Knowledge:

  • 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.

Skills:

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

Competences:

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

See Absalon

First year organic chemistry and mathematics
Videolectures and classroom discussion
  • Category
  • Hours
  • Class Instruction
  • 12
  • Preparation
  • 93,5
  • E-Learning
  • 50
  • Project work
  • 50
  • Exam
  • 0,5
  • Total
  • 206,0
Written
Oral
Individual
Collective
Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
Credit
7,5 ECTS
Type of assessment
Continuous assessment, 3 assignments
Oral exam on basis of previous submission, 30 minutes (no preparation time)
Type of assessment details
3 assignments, each accounting for 8% of the final grade.
Any late assignments will receive the grade -3 and no feedback.

The oral examination account for 76% of the final grade. Submission of the project report is a prerequisite to partake in the oral exam. The exam project is scheduled for block week 7 and 8, and is due at the end of week 8.
The 30-minute individual oral examination is without preparation and based on the exam project report. The exam begins with a 5-minute presentation from the examinee, after which the internal assessors ask questions about the exam project and other course content.

The three assignments combined and the oral exam must each receive a passing grade to pass the course.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Same as the ordinary exam. 

Previously passed parts of the examination do not need to be retaken.

If the student has not passed the assignments, possibly revised assignments must be submitted in the week before the oral re-examination.

If the student has not passed the oral examination, a possibly revised project report must be submitted in the week before the oral re-examination.

Criteria for exam assesment

See Learning Outcome