Computational Medicinal Chemistry

7.5 credits

Syllabus, Master's level, 3FK119

Code
3FK119
Education cycle
Second cycle
Main field(s) of study and in-depth level
Drug Discovery and Development A1N, Pharmaceutical Chemistry 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.

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:

KNOWLEDGE AND UNDERSTANDING

- 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

COMPETENCE AND SKILLS

- 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

JUDGEMENT AND APPROACH

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

Content

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.

Instruction

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

Assessment

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

FOLLOW UPPSALA UNIVERSITY ON

facebook
instagram
twitter
youtube
linkedin