C2136 Advanced chemoinformatics

Faculty of Science
Autumn 2020
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Jaroslav Koča, DrSc. (lecturer)
doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Stanislav Geidl, Ph.D. (assistant)
Mgr. Ing. Crina-Maria Ionescu, Ph.D. (assistant)
Sushil Kumar Mishra, Ph.D. (assistant)
Guaranteed by
prof. RNDr. Jaroslav Koča, DrSc.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science
Prerequisites
C2130 Introduction to chemoinformatics and bioinformatics or C2133 Introduction to chemoinformatics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
In the course, a student will acquire advanced knowledge in the chemoinformatics field. At the end of the course, the students will be able to perform prediction of physico-chemical properties of molecules based on their structure (via QSPR models). Furthermore, the student will acquire experiences with virtual screening and application of docking in this field. The student will be also able to find information about molecues and their properties in chemoinformatics databases. In addition, the student will learn, how to apply modern methods for chemoinformatics data processing - e.g., genetics algrithms, neuron networks, cluster analysis and support vector machines.
Learning outcomes
After finishing of the course, a student will be able to describe in detail key chemoinformatics approaches, he/she will be able to use software tools for thei realization and will be able to apply them in praxis.
Syllabus
  • 1) Models for prediction of physio-chemical properties of molecules based on their structure (QSPR models) – teoretical description of the model
    2) Design of QSPR model and selection of descriptors
    3) Linear and multilinear regression in QSPR model
    4) Quality metrics of QSPR model
    5) Nonlinear QSPR models
    6) Aplication of QSPR models for prediction of seelcted properties (pKa, log P)
    7) Virtual screening in chemoinformatics: “drug-likeness”, virtual screening based on ligand structure, virtual screening based on receptor structure
    8) ADMET descriptors (adsorbce, distribution, methabolism, excretion and toxicity), application of docing in virtual screening, score function
    9-10) Chemoinformatics databases: Database of molecular structures (DTP NCI, Pubchem, Drugbank, Zinc), databases of molecular properties and databases of descriptors. Searching in these databases.
    11-12) Modern methods of chemoinformatics data processing: genetic algorithms, neuron netwrks, cluster analysis, support vector machines
Literature
  • • Chemoinformatics Concepts, Methods, and Tools for Drug Discovery. Edited by Jürgen Bajorath. Humana Press Totowa, New Jersey, 2004
  • • Chemoinformatics Approaches to Virtual Screening. Edited by Alexandre Varnek and Alex Tropsha. Royal Society of Chemistry, 2008
  • Handbook of chemoinformatics :from data to knowledge in 4 volumes. Edited by Johann Gasteiger. Weinheim: Wiley-VCH, 2003, xlvii s.,. ISBN 3-527-30680-3. info
  • Chemoinformatics : a textbook. Edited by Johann Gasteiger - Thomas Engel. Weinheim: Wiley-VCH, 2003, xxx, 649. ISBN 9783527306817. info
Teaching methods
lectures practical demonstrations
Assessment methods
written test
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught once in two years.
The course is taught: in blocks.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2011 - acreditation, Autumn 2013, Spring 2014, Autumn 2014, Autumn 2016, Autumn 2018, autumn 2021, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2020, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2020/C2136