Modul Drug Design

Code:	233500
Module title:	Drug Design
Version:	1.0 (05/2017)
Last update:	31.05.2024
Responsible person:	Prof. Dr. rer. nat. Fester, Karin Karin.Fester@hszg.de

Offered in 6 study courses:	Integrated Management (M.Sc.) valid from class 2020
	Integrated Management (M.Sc.) valid from class 2021
	Integrated Management Systems (M.Sc.) valid from class 2023
	Integrated Management Systems (M.Sc.) valid from class 2025
	Pharmaceutical Biotechnology (M.Sc.) valid from class 2018
	Pharmaceutical Biotechnology (M.Sc.) valid from class 2020

Semester according to time table:	WiSe (winter semester)
Module level:	Master
Duration:	1 semester
Status:	elective core module
Place where the module will be offered:	Zittau
Language of Instruction:	English

Workload* in

SCH **

semester

hours

ECTS Credits

1

2

3

L

S

P

O

L

S

P

O

L

S

P

O

150

5

4.0

2

2

0

0

*	Overall workload per module (1 ECTS credit corresponds to a workload of 30 hours)
**	One semester credit hour (SCH) corresponds to a workload / class meeting of 45 minutes per week in a semester

Self study time in hours	total	subdivided into
	105	60 Preperation of contact hours	45 Preparation of exam	0 Others

Learning and teaching methods:	The knowledge will be communicated by lectures. Practical exercises in drug design will deepen the understanding of bioinformatics theory.

Further information:

none



Exam(s)
Minor exam	assessed assignment (confirmation of attendance)
Assessment	Major written exam	120 min	100.0%

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Syllabus plan/Content:

Lectures: Session 1: General introduction to methods of chemical computation, e.g. DFT, ab initio, molecular mechanics. For each case, show examples and limitations. Session 2: Ab initio and modeling small molecules, quantum mechanics, hamiltonians, basis sets. Session 3: Introduction to force field, force field terms, examples of force fields, etc. Session 4: Solvation of molecular systems, examples of solvent systems, explicit and implicit solvation, implications, etc. Session 5: Energy minimization, methods, limitations. Session 6: Systematic search methods, search algorithms, the complexity of molecular surfaces. Session 7: Molecular dynamics simulations, Monte Carlo, statistical problems, etc. Session 8: Introduction to protein structure, amino acids, primary structure of proteins and peptides. Session 9: Secondary structural features; alpha helices, beta strands, loops. Session 10: Tertiary structure, stability of proteins, examples. Session 11: Quaternary structure, protein domains, functions of proteins. Session 12: RNA: introduction, nucleotides, secondary structure, Ramachandran plots. Session 13: RNA: tertiary structure, worked problems. Session 14: Data sources: the Cambridge crystallographic data centre, the protein data bank, PubChem, ChEMBL, EBI, etc. Session 15: Structural classification of proteins: classes, folds, superfamilies, families. Session 16: The CATH hierarchy: classes, architecture, topology, homologous superfamilies, sequence families. Session 17: The PDB file format, visualizing and interpreting data from a PDB file. Session 18: Introduction to software and computational tools: PyMOL, Swiss Pdb Viewer, MOE Session 19: Introduction to software and computational tools: Maestro, LigandScout, ChemSketch, CDK, etc. Session 20: Introduction to protein sequence alignment Session 21: Local and global alignment algorithms. Session 22: Homology modeling. Session 23: Comparison and superposition of proteins: RMSD, sequence similarity, distance alignment, etc. Session 24: Protein-ligand interactions, introduction to docking. Session 25: Strengths and limitations of docking. Session 26: Docking and scoring, introduction to scoring functions. Session 27: Protein-ligand interactions, introduction to docking. Session 28: Virtual screening, strengths and limitations. Session 29: Introduction to QSAR, 2D QSAR, CoMFA, CoMSIA, etc. Session 30: Introduction to machine learning OR Introduction to genomics. Practical computing exercises: Sessions 1-2: Basic Linux operations Sessions 3-4: File formats (mol, mol2, sdf, etc.) Sessions 5-6: Representation of small molecules (SMILES format, InChI, InChI key, etc.) Sessions 7-8: Searching databases I Sessions 9-10: Searching databases II Sessions 11-12: Substructure searches Sessions 13-14: Modeling small molecules, RMSD calculation. Sessions 15-16: Protein structure Sessions 17-18: Homology modeling Sessions 19-20: Energy minimisation Sessions 21-22: Molecular Dynamics simulation Sessions 23-24: Docking and scoring Sessions 25-26: QSAR Sessions 27-28: Virtual screening Sessions 29-30: Machine learning OR genomics

Learning Outcomes:
Subject-specific skills and competences:	After successfully completing the module, students are able to built protein models and perform docking studies with ligands. They are able to search databases for molecules with high affinity to a given target structure. The students can explain why structure-acivity relationships are important for drug development.
Generic competences (Personal and key skills):	After successfully completing the module, the students are able to think and act across disciplines. They can work independently and develop new strategies and solutions.

Pre-requisites:	none
Optional pre-requisites:	Informatics, biochemistry, medicinal chemistry, molecular biology and organic chemistry

Literature:

1. F. J. Burkowski. Structural Bioinformatics: an algorithmic approach. CRC Press, London, 2009. 2. P. M. Selzer, R. J. Marhöfer, A. Rohwer. Applied Bioinformatics: an introduction. 2nd Edition, Springer, Heidelberg, 2018. 3. H.-D. Höltje, W. Sippl, D. Rognan, G. Folkers. Molecular modeling: basic principles and applications. Wiley, Weinheim, 2008. 4. G. L. Patrick. An Introduction to Medicinal Chemistry.7th Edition, Oxford (UK), 2023. 5. A. Davis, S. E. Ward. The Handbook of Medicinal Chemistry: Principles and Practice. ‎Royal Society of Chemistry, 2014. 6. Basics of Python: https://docs.python.org/3/ 7. Introduction and usage of Galaxy Framework: https://galaxyproject.org/ and https://training.galaxyproject.org/