[ 993TALSALSK19 ] KV Artificial Intelligence in Life Sciences
|
|
|
|
 |
| Workload |
Education level |
Study areas |
Responsible person |
Hours per week |
Coordinating university |
| 3 ECTS |
M1 - Master's programme 1. year |
Computer Science |
Günter Klambauer |
2 hpw |
Johannes Kepler University Linz |
|
|
|
| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2019W |
| Objectives |
This course presents artificial intelligence methods in Life Sciences, concretely in drug discovery, pharmacology, toxicology and bioinformatics. The course will demonstrate the application and use of machine learning in Life Sciences, with the focus on the field of small molecules and drug design. Both theoretical foundations of methods as well as basics of the implementation of those will be given.
|
| Subject |
- Basic machine learning methods in Life Sciences
- Molecular representation
- Chemical structure information
- Chemical descriptors and fingerprints
- Advanced machine learning methods in Life Sciences
- Molecule kernels
- QSAR
- Virtual Screening
- Drug target prediction
- Graph convolutions
- Multi-task learning
- Active learning
- Generative models for molecules
|
| Criteria for evaluation |
|
| Language |
English |
| Changing subject? |
No |
|
|
|
| On-site course |
| Maximum number of participants |
- |
| Assignment procedure |
Direct assignment |
|
