Molecular Modelling

Postgraduate course

Course description

Objectives and Content

This course aims to introduce students to the principles and techniques of molecular modelling, with a focus on biomolecular applications. For students enrolled in the Civ. Eng. In Data Science program the successful completion of the course qualifies for 5 credits for physics and 5 credits for chemistry.

Learning Outcomes

  • On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
  • Knowledge

    The student

  • has a general understanding of the concept of statistical mechanics ensembles
  • can identify the length and time scales suitable for molecular dynamics simulations
  • knows what a classical forcefield is and comprehends the possibilities and limitations of the classical approximation
  • forcefields of Class I (general form) vs Class II (cross-terms)
  • basis of forcefield parametrization
  • is familiar with key molecular dynamics concepts, such as integrators, periodic boundary conditions, and treatment of electrostatics
  • knows the principle of the Monte Carlo sampling
  • knows how to extract free energies from molecular simulations
  • understands the possibilities and limitations of docking approaches
  • is able to extract relevant molecular properties from simulations
  • Skills:

    The student:

  • can explain the underlying chemical, physical, and mathematical principles of molecular modelling and simulation
  • selects and applies appropriate modelling approaches to different molecular systems
  • Set up and performs molecular dynamics simulations and docking searches using Unix/Linux
  • Analyzes simulation results and extracts meaningful information
  • Visualizes molecular structures and trajectories for learning purposes
  • General Competences:

    The student is:

  • Able to reflect on the application of molecular modelling in addressing chemistry-related questions, and evaluate the suitability of modelling approaches for specific problems
  • Able to recognize theoretical frameworks in new simulation techniques
  • Level of Study


    Semester of Instruction

    Required Previous Knowledge
    Teaching and learning methods
    Lectures, computer exercises using Python notebooks, reports.
    Compulsory Assignments and Attendance
    Compulsory work: Approved exercises. Approved exercises are valid for five following semesters.Compulsory work must be submitted within the given deadlines for the course. Approval of the compulsory work is necessary to get admittance to the written exam.
    Forms of Assessment

    The form of assessment is portofolio consisting by:

    ·         Approved tutorials during the semester, 20% of total grade.

    ·         Written examination (4 hours), 80% of total grade.

    Grading Scale
    The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
    Assessment Semester
    Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.]
    Reading List
    The reading list will be available within July 1st for the autumn semester and December 1st for the spring semester
    Course Evaluation
    The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department
    Examination Support Material
    Examination support materials: Non- programmable calculator, according to model listed in faculty regulations.
    Programme Committee
    The Programme Committee is responsible for the content, structure and quality of the study programme and courses.