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Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2023
Content and learning outcomes
Course contents
The course consists of both theoretical lectures and practical computer exercises. The following topics will be discussed:
Basic quantum chemistry: Molecular orbital theory, semi-empirical methods
Basic density functional theory (DFT)
Molecular mechanics and molecular dynamics
Monte Carlo methods
Energy minimization and potential energy surfaces
QM/MM methods
Solvation and surrounding effects
Theoretical methods in drug discovery: Docking, protein structure prediction, QSAR
Simulation of chemical reactions in solution
Modelling of enzymatic catalysis
Field trip to pharmaceutical company
Intended learning outcomes
Today, computer simulations are an important tool for the study of chemical processes in such different systems as isolated molecules, fluids, polymers, solid state, and biological macromolecules, like proteins and DNA. The enormous development of computer hard drive space means that the molecular modeling field is developing very quickly.
The goal with this course is to provide an overview of the methods and techniques which are used within modern molecular modeling. Basic theory will be covered and applications within chemistry, biochemistry and medicinal chemistry will be discussed.
Learning activities
The course is taght with lectures, student presentations of research papers, and computer exercises. The computer exercises and the presentation of a research paper is mandatory.
Detailed plan
Learning activities
Content
Preparations
Lecture 1 (28 Oct)
Introduction + Potential energy surfaces
Lecture 2 (30 Oct)
Molecular Mechanics
Lecture 3 (4 Nov)
Molecular dynamics
Lecture 4 (7 Nov)
No lecture!
Lecture 5 (11 Nov)
Applications of Molecular Dynamics
Lecture 6 (13 Nov)
Introduction to QM
Lecture 7 (18 Nov)
Hartree-Fock, Basis sets
Lecture 8 (20 Nov)
Correlated Methods
Lecture 9 (25 Nov)
Density Functional Theory
Lecture 10 (27 Nov)
Applications of Quantum Chemistry
Lecture 11 (2 Dec)
Solvation models
Lecture 12 (4 Dec)
Qualitative Structure Activity Relation (QSAR) in Drug Discovery
Lecture 13 (9 Dec)
Molecular Docking in Drug Discovery
Lecture 14 (11 Dec)
Research paper presentation
Mandatory to present one paper
Lecture 15 (TBA)
Research paper presentation
Lecture 16 (TBA)
Invited lecture
Lecture 17 (After X-Mas)
Summary and repetion
Preparations before course start
Recommended prerequisites
Basic classes in chemistry and mathematics för K och BIO.
Specific preparations
All lectures and exercises will be in room 304 at Teknikringen 14.
Literature
No information inserted
Equipment
We strongly encourage you to bring your laptop to the lectures (Mac, Windows, or Linux should work). We will perform computations in a Python environment during the lectures and you will be able to compute quantum chemistry, molecular dynamics and more simultaneously with the lectures. No prior Python experience is necessary.
All computer exercises will be performed on your own laptop.
Software
You need to install either Anaconda or Miniconda (not both) on your computer for the exercises. If you have no prior installation we suggest you to chose Miniconda
LAB1 - Laboratory Work, 1.5 credits, Grading scale: A, B, C, D, E, FX, F
TEN1 - Written exam, 6.0 credits, Grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
