Welcome to the website for Applied Mathematics and Informatics In Drug Discovery (AMIDD), the course series running at the Department of Mathematics and Informatics, University of Basel in the fall semester 2023.
The course series introduces interdisciplinary research in drug discovery with mathematics as the language and computation as the tool. We have a diverse and lively class room that learn together and from each other: every year about two third students of the class study mathematics or computer science, while other students study physics, chemistry, (computational) biology, pharmacy, and other fields such as epidemiology and medicine.
More information on the course can be found at the course directory of the University Basel.
Table of content
- Time and place
- Course material and licensing
- Pre-course survey
- Lecture 1: introduction (22.09.2023)
- Lecture 2: Drug targets and mechanistic modelling (29.09.2023)
- Lecture 3: Statistical modelling and causal inference in drug discovery (06.10.2023)
- Lecture 4 (13.10.2023)
- Lecture 5 (20.10.2023)
- Lecture 6 (27.10.2023)
- Lecture 7 (03.11.2023)
- Lecture 8 (10.11.2023)
- Lecture 9 (17.11.2023)
- Dies academicus (24.11.2023, no lecture)
- Lecture 10 (01.12.2023)
- Lectuer 11 (08.12.2023)
- Lecture 12: guest lectures (15.12.2023)
- Lecture 13: a collaboration challenge (22.12.2023)
- Further questions or suggestions?
- Archives of past courses
Time and place
The lecture takes place on Fridays between 12:15 and 14:00 at Spiegelgasse 5, Seminarraum 05.002.
Course material and licensing
Course material, including lecture notes, slides, and reading material, are shared on the course’s web site, 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.
Prior to attending the first session, please fill out the voluntary pre-course survey. Your reply helps me to shape the course to meet your needs.
The final note is given by participation including quizzes (30%), offline activities (40%), and a collaboration challenge in the final session (30%).
Lecture 1: Introduction
The first module is finished with the first lecture.
- Please fill the anonymous post-lecture survey of Lecture 1.
- Offline activities of Lecture 1: Check out this poster, which illustrates top 200 brand name drugs by retail sales in 2022. You can also find a snapshot in the slide deck. Focus on the top 14 compounds, and answer the questions by filling out this form.
Lecture 2: Drug targets and mechanistic modelling
- Slides of lecture 2 on drug targets and mechanistic modelling.
- Please fill the anonymous post-lecture survey of Lecture 2.
- Offline activities of Lecture 2:
- (Optional) Watch the video tutorials hyperlinked in the slides if you want to get familiar with protein structure and protein function classes.
- Watch a video on the development of the drug Herceptin, presented by Susan Desmond-Hellmann and answer questions. See the questions and submit your answers here via a Google Form.
- Required reading: Principles of early drug discovery by Hughes et al.
Lecture 3: Statistical modelling and causal inference in drug discovery
Lecture 4 and 5: Molecular modelling
Lecture 6 and 7: Omics- and cellular modelling
Lecture 8 and 9: Organ- and system modelling
Note that on Friday, 24.11.2023, there is NO lecture due to Dies Academicus.
Lecture 10 and 11: Population modelling
Lecture 12: Invited talks
Lecture 13: A Collaboration Challenge
Details will be announced.
Further questions or suggestions?
Please contact the lecturer, Jitao David Zhang, at email@example.com.