Computational Medicinal Chemistry

7.5 credits

Syllabus, Master's level, 3FK219

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Drug Discovery and Development A1N, Pharmaceutical Sciences A1N
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Educational Board of Pharmacy, 25 April 2019
Responsible department
Department of Medicinal Chemistry

General provisions

The course is given in English.

This course substitutes and corresponds to 3FK119.

Entry requirements

For students in the pharmacy programme; At least 150 credits from semester 1-7 of which 60 credits pharmaceutical chemistry including organic chemistry and medicinal chemistry.

For the Master of Science Programme in Chemical Engineering, at least 150 credits are required within the programme including organic chemistry and medicinal chemistry.

Admitted to the Master's programme (two-year) in biomedicine.

Admitted to the Master Programme in Drug Discovery and Development.

Admitted to the Master Programme in Pharmaceutical modelling.

For single subject course: 150 credits including at least 22.5 credits organic chemistry of which 10.5 credits at level B or the equivalent knowledge which is evaluated on an individual basis. Knowledge in English equivalent to what is required for general entry claims to Swedish first-cycle programmes.

Learning outcomes

On completion of the course, the student should be able to:


- explain how a molecular mechanics force field is constructed

- account for the basics in PCA and PLS modelling

- account for the basics in molecular recognition

- describe strategies to convert peptides to drug-like molecules


- apply 3D-QSAR methodology to predict biological effects

- dock molecules to a target protein

- carry out conformational analysis studies

- carry out a database search to identify molecules that satisfy different pharmacophore criteria

- calculate physico-chemical descriptors and use these in QSAR analyses

- use multivariate techniques to select datasets based on molecular diversity

- build and use in silico models to predict drug absorption

- rank and select molecules based on their predicted biological effects and properties

- orally present and discuss scientific results in English


- demonstrate insight of the use of predictive models to promote sustainable development


The course gives an introduction to methods and strategies that are used in drug discovery with a focus on computer-aided drug design. The course includes components such as conformational analysis, ligand-based design, peptide modelling, structure-based design, multivariate analysis, and 3D-QSAR. The content of the course is to a large extent included in computer exercises that includes training in software used in drug discovery projects in the industry.


The teaching is given in the form of lectures, seminars and computer exercises. All computer exercises are compulsory.


Examination is arranged at the end of the course. A passing grade requires passed individual oral presentation in English of a project work, a passed written examination, and participation in all compulsory parts of the course.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator.

Completion of compulsory parts takes place by agreement with the course coordinator.

No reading list found.