summer semester 2024

Artificial Intelligence for Drug Design (Module 13979)

Learning Outcome
After successfully completing the module, students have insight into this exciting field of application for Artificial Intelligence (AI). They are able to acquire research literature and to present the topic orally as well as in a written report.

AI is revolutionizing drug design both for small molecule drugs - the prevalent drug modality - and novel modalities such as oligonucleotide therapeutics. Some of the progress has been achieved by transferring methods from established AI areas such as NLP. For other areas novel methodological developments were instrumental, with very exciting developments on the intersection between molecular dynamics and AI. The focus of the seminar will be on state-of-the-art methods and applications of AI in drug design for small molecule drugs and oligonucleotide therapeutics.

Recommended Prerequisites
Working knowledge of probability/statistics and modern machine learning methods.

Bioinformatics: Artifical Intelligence and Algorithmic Approaches (Module 13978)

Learning Outcome
After successfully completing the module, students will have acquired an introduction to modern bioinformatics and to selected applications from biology and medicine. They understand the methodology through presentation of the central computational problems and an introduction of solutions based on classical algorithms and statistical machine learning, as well as modern deep learning approaches.

The focus will be on four fundamental problem areas:

  • Comparing sequences: Sequence alignment algorithms, Genomescale approaches using index data structures, Alignment-free methods
  • Analyzing gene expression: alignment-based and alignment-free methods to analyzing RNASeq, single-cell analysis
  • Signals in sequences: identification of motifs, accessibility, and modification of DNA
  • Sequence variations and relation to phenotypes: structural variants in disease, pan-genome approaches

Recommended Prerequisites

  • Basic knowledge of probability and statistics, algorithms and data structures at the undergraduate level
  • Introduction to machine learning at Master's level
  • Working knowledge of Python