Written or oral exam

Starting with an overview of its main application area, namely drug design, the lecture teaches how computer science methods can be used to work with chemical data, strongly focusing on small organic molecules (compounds). Digital representation of compounds (graphs, line notations, file formats) is followed by most important strategies of topological comparison (identity, substructure, similarity). Relevant applications of topological similarity are introduced (searching, clustering, library generation). Quantitative Structure-Activity Relationship (QSAR) is introduced as the cheminformatics branch for predictive modeling of chemical properties. Finally, the prediction of 3D-structures from topology and similarity methods based on molecule geometry are introduced.

Students know how different kinds of chemical data can be handled with computers, how to represent and to analyse that data with methods from computer science, and they have an overview of the main application area drug design. Having understood the fundamental SSimilar Property Principle, they are able to handle and to analyse experimental screening data and to implement and apply ligand-based screening methods. Students have a solid knowledge on standard tools and software libraries for cheminformatics. Project work strengthened their ability to work in a team and to write down and to present scientific work.

Notwendige Vorkenntnisse:
- Keine formellen Anforderungen
- Grundkenntnisse in organischer Chemie und Graphentheorie
- Programmierkenntnisse in Python

Requirements:
- No formal requirements
- Basic knowledge of organic chemistry and graph theory
- Programming skills in Python

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Literature / Materials:
Lecture slides and additional materials will be provided digitally. Basic knowledge of organic chemistry, graph theory, and programming skills in Python are recommended.

Recommended textbooks:
1) Leach A., Gillet V. An Introduction To Chemoinformatics", Springer 2007
2) Faulon J.-L., Bender A. (Eds.) "Handbook Of Chemoinformatics Algorithms", CRC Press 2010
3) Engel T., Gasteiger J. (Eds.) Chemoinformatics", Wiley-VCH 2018
4) Optional: Klebe G. "Wirkstoffdesign", Springer 2009