Students:

• Will be well informed about molecular data storing and processing.

• Will be able to assess a similarity between organic structures.

• Will be able to construct chemical libraries with required physico-chemical properties.

• Will be able to predict biological activity from the structure.

• Will understand algorithms used in cheminformatics applications.

A credit is awarded based on the solution of problems during seminars.

Zápočet se uděluje na základě řešení problémů během seminářů.

R: Leach A. R. - An Introduction to Chemoinformatics, Springer, 2007, ISBN 1402062907

R: Brown N. - In Silico Medicinal Chemistry: Computational Methods to Support Drug Design, RSC, 2015, ISBN 1782621636

A: Bajorath, J. - Chemoinformatics for Drug Discovery, Wiley, 2013, ISBN 1118139100

A: Engel T., Gasteiger T. - Chemoinformatics: Basic Concepts and Methods, Wiley-VCH, 2018, ISBN 3527331093

A: Bunin B. A., Siesel B. Morales G., Bajorath J. - Chemoinformatics: Theory, Practice, & Products, Springer, 2010, ISBN 9048172500

oral

ústní

1. Therapeutics, pharmacodynamics - ligand binding, molecular targets
   

   Introduction to the fundamentals of therapeutics and pharmacodynamics, focusing on ligand binding and identifying molecular targets.



2. Pharmacodynamics - affinity, potency, efficacy, pharmakokinetics - ADME, Tox
   

   Study of the key concepts in pharmacodynamics, including affinity, potency, efficacy, and an overview of pharmacokinetics (Absorption, Distribution, Metabolism, Excretion) and toxicity.



3. Drug discovery & development, molecular target identification and validation, high-throughput screening, bioassays
   

   Comprehensive understanding of the drug discovery and development process, including molecular target identification and validation.



4. Tour of the HTS facility at the Institute of Molecular Genetics, Czech Academy of Sciences
   

   An educational tour to provide practical insights into high-throughput screening (HTS) processes and technologies.



5. Fragment-based drug design, Morgan algorithm, SMILES, InCHI
   

   Exploration of fragment-based drug design, understanding the Morgan algorithm, and learning about linear chemical structure notations SMILES and InCHI.



6. Chemical data preparation and curation, fingerprints, similarity coefficients
   

   Training in the preparation and curation of chemical data, understanding molecular fingerprints and similarity coefficients.



7. Similarity search, molecular descriptors
   

   Skills in conducting similarity searches and understanding molecular descriptors.



8. Diversity selection, pharmacophore modelling, scaffold hopping
   

   Techniques in diversity selection, pharmacophore modelling, and scaffold hopping in drug design.



9. QSAR modeling - bias and variance, random forest, AdaBoost
   

   Detailed study of Quantitative Structure-Activity Relationship (QSAR) modeling, focusing on bias, variance, and machine learning methods like Random Forest and AdaBoost.



10. QSAR modeling - Gradient Boost, hyperparameter optimization
    

    Advanced QSAR modeling techniques, including Gradient Boost and hyperparameter optimization strategies.



11. QSAR model validation, applicability domain
    

    Procedures for validating QSAR models and understanding their applicability domain.



12. Structure-based drug design, molecular docking
    

    Insight into structure-based drug design and techniques in molecular docking.



13. Chemoinformatics software and database technologies.
    

    Introduction to various software and database technologies used in chemoinformatics.



14. Use cases in computational drug design
    

    Real-world case studies to apply the learned principles and techniques in computational drug design.

none

Biochemistry, Organic chemistry