SubjectsSubjects(version: 949)
Course, academic year 2021/2022
Advanced chemoinformatics - P143001
Title: Pokročilá chemoinformatika
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019 to 2022
Semester: winter
Points: winter s.:0
E-Credits: winter s.:0
Examination process: winter s.:
Hours per week, examination: winter s.:3/0, other [HT]
Capacity: unlimited / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
For type: doctoral
Note: course is intended for doctoral students only
can be fulfilled in the future
Guarantor: Svozil Daniel prof. Mgr. Ph.D.
Is interchangeable with: AP143001
Annotation -
Last update: Svozil Daniel prof. Mgr. Ph.D. (07.06.2018)
The class covers advanced chemoinformatics and computational drug design techniques, such as lead optimization, biological information in models or the generation and exploration of chemical space.
Aim of the course -
Last update: Svozil Daniel prof. Mgr. Ph.D. (07.06.2018)

Students will:

  • understand advanced chemoinformatics and computationa drug design techniques
  • be able to assess the quality of QSAR classification and regression models
  • optimize lead structures using in silico techniques
  • understand the process of chemical space exploration and the assessment of its chemotype diversity
Literature -
Last update: Svozil Daniel prof. Mgr. Ph.D. (07.06.2018)


R: Engel T. Gasteiget J. Applied Chemoinformatics: Achievements and Future Opportunities, Wiley-VCH, 2018, ISBN 352734201X

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

Learning resources -
Last update: Svozil Daniel prof. Mgr. Ph.D. (07.06.2018)

Online course materials

Syllabus -
Last update: Svozil Daniel prof. Mgr. Ph.D. (07.06.2018)

Chamoinformatics methods for lead optimization - MMPA (matched molecular pairs analysis), bioisosters, scaffold hopping, multi-objective optimizationoptimalizační metody

Biological information in chemeoinformatics – chemogenomics space, experimental and computational approaches of chemogenomics space exploration, affinity fingerprints and their applications, proteochemometrics, ligand/protein interaction descriptors, protein/ligand interaction space modeling

Information theory and fingerprint engineering

QSAR modeling – QSAR model quality assessment, applicability domain in classification and regression models, deep learning methos in QSAR and their other applications

Generating and exploration of chemical space, chemotype diversity and its assessment, pharmacophore modeling (topological pharmacophores and pharmacophore fingerprints), molecular docking (conformer generation, protein flexibility, consensus scoring)

Course completion requirements -
Last update: Svozil Daniel prof. Mgr. Ph.D. (07.06.2018)

Oral exam