Chemoinformatics

• 26 hrs lectures
• 26 hrs pc labs

The aim of the course is to introduce the participants to various chemoinformatics methods, to show examples of the use of chemoinformatics in modern drug research, and to give the participants practical experience through hands-on chemoinformatics exercises.
A student who has met the objectives of the course will be able to:
- Define chemoinformatics and name the main areas of application within drug discovery.
- Interpret the most important formats used for describing molecular structures
- Describe the most widely used machine learning tools in chemoinformatics and the algorithms that they are based on.
- Understand the differences between linear and non-linear models, supervised and unsupervised machine-learning, clustering and classification.
- Argue on how to choose the appropriate computational tools for a given problem.
- Describe rational work flows for data mining and for preparing high quality data sets for modeling purposes.
- Interpret the output from and evaluate the performance of a given computational tool.
- Navigate and extract information from annotated chemical libraries.
- Construct and interpret drug-protein interaction networks.
- Plan, carry out and present computer exercises and mini-projects as team work.
- Be able to evaluate your own work and relevant scientific articles.
- Create scientific posters and present projects orally.

The generic knowledge on the Bachelor´s degree level is requested, namely in the area of molecular biology, biochemistry and bioinformatics.

• Walter Filgueira de Azevedo: Docking Screens for Drug Discovery(Methods in Molecular Biology), 2019, Humana, 978-1493997510
• Engel, T., Gasteiger, J.: Chemoinformatics: Basic Concepts and Methods, 2018, Wiley, 978-3527331093
• Barry A. Bunin et al.: Chemoinformatics: Theory, Practice and Products, 2007, Springer, 978-1402050008

1. Introduction to cheminformatics
2. Concepts and techniques in cheminformatics
3. Molecular docking in drug discovery
4. Virtual screening in drug discovery
5. in-silico ADMET in drug discovery
6. Introduction to pharmacophores
7. Homology modeling
8. Molecular dynamics and simulations
9. Immunological and biological background to chemoinformatics
10. Advances in genomics and proteomics vs drug and vaccine development
1. Úvod do cheminformatiky
2. Koncepty a techniky v cheminformatice
3. Molekulové dokování jako nástroj pro návrh léčiv
4. Virtuální screening jako nástroj pro  návrh léčiv
5. in-silico ADMET jako nástroj pro  návrh léčiv
6. Úvod do farmakoforů
7. Homologické modelace
8. Molekulární dynamika a simulace
9. Imunologický a biologický základ chemoinformatiky
10. Pokroky v genomice a proteomice v porovnání s vývojem léčiv a vakcín 

1. Practical’s on Databases and Webservers
2. Practical’s on Protein and Ligand Preparation
3. Molecular Docking (Basic)
4. Molecular Docking (Advanced)
5. Virtual Screening
6. Pharmacophores
7. Homology Modeling
8. in-Silico ADMET
9. Molecular Dynamics
10. General Practical Session and Doubt Clearance
1. Praktické cvičení na databáze a webové servery
2. Praktické cvičení na přípravu proteinů a ligandů
3. Molekulární docking (základní)
4. Molekulární docking (pokročilé)
5. Virtuální screening
6. Farmakofory
7. Homologické modelování
8. in-Silico ADMET
9. Molekulární dynamika
10. Obecná praktický úkol a diskuze 

Requirements for completion of a course are elaborated by the lecturer responsible for the course every year; basically:
- obtaining at least 10 points (out of 12 as course-unit credit based on active presence in demonstration exercises),
- conductiong individual project and making its presentation in poster and oral form (at least 14 points out of 28 points),
- obtaining at least 30 points in final written exam (out of 60 points).
Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Basically:
- obligatory computer-lab tutorial
- voluntary lecture