English

Not applicable.

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.

Not applicable.

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations. Techning methods include lectures and computer laboratories. Course is taking advantage of e-learning (Moodle) system. Students have to write a project/assignment during the course.

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).

1. Data sets: Extraction of data from a large database, evaluation of structural diversity.
2. Molecular structures: Graphical representation, 1D, 2D and 3D molecular structures, pharmacophores.
3. Molecular descriptors: Generation of descriptors reflecting the physical and chemical properties of the molecules. Molecular fingerprints.
4. Properties: Calculation of physical chemical properties such as solubility and partition coefficients, pharmacological properties such as absorption and toxicity, and global properties like oral bioavailability and drug-likeness.
5. Data analysis: Self-organizing maps, principal component analysis, artificial neural networks, decision trees, support vector machines and others.
6. Applications of chemoinformatics in drug research: Chemical libraries, chemogenomics libraries, virtual screening, protein-ligand interactions and interaction networks, ligand activity profiling, quantitative structure-activity relationships (QSARs), and prediction of ADMET properties (absorption, distribution, metabolism, elimination and toxicity).
7. Tools: Internet-based programs, databases, in-house and commercial programs.

Not applicable.

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.

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

Not applicable.

Not applicable.

D. C. Young: Computational Drug Design. Wiley, 2009. (CS)

A. R. Leach: Molecular Modelling - Principles and Applications (2 edition). Pearson Education, 2001. (CS)
D. C. Young: Computational Chemistry, a Practical Guide for Applying Techniques to Real World Problems. Wiley, 2001. (CS)