PV290 Chemoinformatics

Faculty of Informatics
Autumn 2024
Extent and Intensity
1/1/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
In-person direct teaching
Teacher(s)
doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
Mgr. Vladimír Horský, Ph.D. (assistant)
RNDr. Tomáš Raček, Ph.D. (assistant)
Guaranteed by
doc. RNDr. Radka Svobodová, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Tue 24. 9. to Tue 17. 12. Tue 10:00–11:50 B411
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 40 student(s).
Current registration and enrolment status: enrolled: 13/40, only registered: 0/40, only registered with preference (fields directly associated with the programme): 0/40
fields of study / plans the course is directly associated with
Course objectives
The course is aimed at acquiring knowledge in the field of chemoinformatics. The objectives of the course are as follows: - To explain the concept of molecular structure (1D, 2D and 3D) and methods of its representation in computer. - To explain the most important chemoinformatics methodologies for dealing with the structures of small organic molecules (including their algorithms and complexity). - Explain how to work with and search databases of small molecules.
Learning outcomes
Upon successful completion of the course, the student will be able to: - explain the concept of chemoinformatics - describe the types of structure of molecules and their representations - describe basic chemoinformatics methodologies for working with small organic molecules (including their algorithms and complexity) and demonstrate their application with selected examples - search for structures of molecules in databases and visualise them - describe the methodology for generating structures of molecules using AI Translated with www.DeepL.com/Translator (free version)
Syllabus
  • 1. Introduction, concept of chemoinformatics, content of the subject, history of the field 2. 1D, 2D and 3D structure of a molecule, data representation of a molecule using graph and matrix 3. 2D structure (topology) of a molecule, writing a molecule using a string (SMILES, InChi, InChiKey) 4. Isomorphism and canonical indexing of molecular graphs 5. Cycle search, fingerprints 6. 3D structure (geometry) of the molecule, representation using Cartesian and internal coordinates, data formats, geometry comparison 7. Molecular descriptors 8. Similarity of molecules, similarity comparison, similarity coefficients 9. Models for studying quantitative relationships between structure and activity/property of molecules (QSAR and QSPR models) 10. Databases of small and large molecule structures, searching them 11. Visualization of structures of molecules and molecular fragments, models for visualization of molecules 12. Generation of molecule structures using AI algorithms Translated with www.DeepL.com/Translator (free version)
Literature
  • Chemoinformatics :a textbook. Edited by Johann Gasteiger - Thomas Engel. Weinheim: Wiley-VCH, 2003, xxx, 649 s. ISBN 3-527-30681-1. info
Teaching methods
lectures with practical demonstrations, exercises - solving examples and working with data and chemoinformatics web applications on a laptop
Assessment methods
Written test.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2024/PV290