Universität Wien

323103 VU Data science approaches for advancing drug discovery - MPS5 (2021S)

2.00 ECTS (1.00 SWS), SPL 32 - Pharmazie
Continuous assessment of course work
Moodle

Details

Language: English

Lecturers

Classes

Introductory Meeting ("Vorbesprechung"):
March 2, 2021, 9:00-9:30 am, online meeting (link to the online meeting will be provided via moodle)

Information

Aims, contents and method of the course

Data science has become an important field in drug discovery and development nowadays. It offers a level of understanding of health, disease and treatment on a scale never before imagined, i.e. it can help researchers to find new drugs or re-use old drugs for new indications.

In this lecture and seminar we will talk about existing data sources in the open domain, database schemes/structures, database managment systems, data querying, statistical learning, machine learning, chemical data representations used in the domain of life sciences. We will further introduce you into the data management/manipulation tool KNIME, and give some basic introduction into SQL and jupyter notebooks (python).

Content of the lectures:

- VO 1: Traditional drug discovery paradigms and rational drug discovery
- VO 2: Introduction to data-driven drug discovery and translational medicine
- VO 3: Statistics for data sciences: distributions, correlations, hypothesis testing
- VO 4: Statistical Learning/Machine Learning Part 1
- VO 5: Statistical Learning/Machine Learning Part 2
- VO 6: Chemical Data: chemical structure representations, chemical descriptors
- VO 7: Data sources, data integration
- VO 8: Database schemes/structures, data querying

Data Science put into praxis:

The practical part will be performed in the form of either a Journal Club presentation or a small data science project conducted by each student; in the end every student will give a final presentation

Assessment and permitted materials

There will be *no* written exam this semester (due to the COVID-19 pandemics and resulting requirement of distant learning). The assessment will be based on the active contribution during the lectures and on basis of the final presentations and/or the data science project (performed by each student).

Minimum requirements and assessment criteria

Active contribution during the Practical part;
Presence at the 8 lectures;
Journal Club presentation or Executed Data Science Project at the end of the course;
material for further reading will be pointed to in each individual lecture

Examination topics

Content of the lectures; material for further reading; Journal Club papers; Data Science Project;

Reading list

Association in the course directory

Last modified: We 10.02.2021 11:49