SubjectsSubjects(version: 953)
Course, academic year 2023/2024
  
Computational Drug Design - AM143007
Title: Computational Drug Design
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2023
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Level:  
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Svozil Daniel prof. Mgr. Ph.D.
Interchangeability : N143052
Annotation
This course provides an in-depth exploration of the computational methods used in the field of drug discovery and development. It is tailored for students interested in pharmacology, medicinal chemistry, and computational biology. The course bridges the gap between theoretical concepts and their practical applications in the pharmaceutical industry. Emphasis is placed on understanding the pharmacodynamics of drugs, drug discovery methodologies, and the use of chemoinformatics in drug design.
Last update: Svozil Daniel (18.12.2023)
Aim of the course

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.
Last update: Cibulková Jana (03.12.2023)
Literature

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

Last update: Svozil Daniel (18.12.2023)
Learning resources

none

Last update: Cibulková Jana (03.12.2023)
Syllabus

  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.

Last update: Svozil Daniel (18.12.2023)
Registration requirements

Biochemistry, Organic chemistry

Last update: Svozil Daniel (13.12.2023)
Course completion requirements -

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

Last update: Svozil Daniel (15.02.2024)
 
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