SubjectsSubjects(version: 954)
Course, academic year 2019/2020
Advanced chemoinformatics - AP143001
Title: Advanced chemoinformatics
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 3/0, other [HT]
Capacity: winter:unlimited / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Note: course is intended for doctoral students only
can be fulfilled in the future
you can enroll for the course in winter and in summer semester
Guarantor: Svozil Daniel prof. Mgr. Ph.D.
Interchangeability : P143001
Examination dates   Schedule   
Annotation -
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.
Last update: Pátková Vlasta (08.06.2018)
Aim of the course -

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
Last update: Pátková Vlasta (08.06.2018)
Literature -


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

Last update: Pátková Vlasta (08.06.2018)
Learning resources -

Online course materials

Last update: Pátková Vlasta (08.06.2018)
Syllabus -

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

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)

Last update: Pátková Vlasta (28.06.2018)
Course completion requirements -

Oral exam

Last update: Pátková Vlasta (08.06.2018)