Drug design

obligatory courses

Remote learning

The aim of the course is to present the basics of computational design of biologically active compounds.

1) Introduction to drug design

2) Review of the background knowledge

- the most important types of non-covalent interactions

- thermodynamic description of interactions

- 2D vs 3D structure, polymorphisms, isomers and conformations

- molecular modeling methods: force fields, conformational space and its sampling

3) Macromolecules as a drug target.

- primary, secondary, tertiary and quaternary structure of proteins; prosthetic groups and ligands

- structure of nucleic acids

- modeling of protein structure and dynamics: comparative modeling and molecular dynamics

4) Protein-ligand interactions

- protein surface and its properties, active site, binding pocket

- antagonist, agonist, inhibitor

- docking algorithms, scoring functions

5) Elements of chemoinformatics

- databases of chemical particles and their searches

- SMILES

- graph-based and other common algorithms

- combinatorial chemistry

6) Drug design strategies

- leading structure

- drug pharmacophore

- Lipiński's rule

- pharmacodynamics and pharmacokinetics

7) Proteins as therapeutic agents

- design of artificial proteins

- antibodies

8) Application of machine learning

Erland Stevens, "Medicinal Chemistry The Modern Drug Discovery Process", PEARSON

After finishing the course student:

- knows typical drug design problems,

- can analyze the structure and function of biomolecular systems related to disease processes,

- can analyze the properties of both small molecules and the receptor

- can use acquired knowledge in other fields, e.g. in medical diagnostics and in medical biology,

- is aware of the responsibility for the research, experiments or observations undertaken,

- understands and appreciates the importance of intellectual honesty in their own and other people's actions; acts ethically,

- can formulate opinions on basic bioinformatics issues,

- is able to see the limitations of his own knowledge and the need to constantly supplement and update it.

To pass the laboratories, it is necessary to:

- attend classes and submit reports (typically a Jupyter notebook or a Python script)

- finish semester’s project

To pass the lecture, it is necessary to:

- pass the laboratories

- write a theoretical exam comprising several open questions

- two exam terms will be scheduled plus an “early-bird” term

Phd students additionally should complete a research project within the laboratory.