Welcome to the website for Applied Mathematics and Informatics In Drug Discovery (AMIDD)!

AMIDD runs at the Department of Mathematics and Informatics, University of Basel, annually in the fall semester. The course series introduces interdisciplinary research in drug discovery with mathematics as the language and computation as the tool.

We welcome bachelor, master, and PhD students of diverse backgrounds including (but not limited to) mathematics, computer science, physics, chemistry, (computational) biology, pharmacy, and other fields such as epidemiology and medicine.

The course is in-person only. Remote or virtual attendance is unfortunately not feasible. We have a diverse and lively class room that work with and learn from each other interactively, which is challenging in a virtual or hybrid setting.

More information on the course can be found at the course directory of the University Basel.

Table of content

Time and place

The lecture takes place on Fridays between 12:15 and 14:00 at Spiegelgasse 5, Seminarraum 05.002. In-person attendance is required.

Course material and licensing

Course material, including lecture notes, slides, and reading material, are shared on this web site, http://AMIDD.ch, unless otherwise specified in the course.

All course material, unless otherwise stated, is shared under the Creative Commons (CC-BY-SA 4.0) license.

Pre-course survey

The URL will be provided soon.

Assessment

The final note is given by participation including in-class quizzes (30%), offline activities (40%), and a collaboration challenge in the final session (30%).

Syllabus

Lecture 1: Introduction to drug discovery

The first lecture introduces drugs and drug discovery.

Lecture 2: The What, the Who, and the How of drug discovery

Lecture 3: Key questions in drug discovery

Lecture 4: Proteins as drug targets

Lecture 5: Protein-ligand interaction

Lecture 6: Statistical model and machine learning

Lecture 7: Causal inference

Lecture 8: Lead identification and optimization

Lecture 9: Mechanism and mode of action of drug candidates

Lecture 10: DMPK and PKPD modeling

Lecture 11: Guest lecture

Lecture 12: A collaboration challenge

Offline activities

Further questions or suggestions?

Please contact the lecturer, Jitao David Zhang, at jitao-david.zhang@unibas.ch.

Archives of past courses