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
- Course material and licensing
- Pre-course survey
- Assessment
- Syllabus
- Lecture 1: Introduction to drug discovery (20.09.2024)
- Lecture 2: The What, the Who, and the How of drug discovery (27.09.2024)
- Lecture 3: Key questions in drug discovery (04.10.2024)
- Lecture 4: Biological foundation of drug discovery (11.10.2024)
- Lecture 5: Protein-ligand interaction (18.10.2024)
- No lecture on 25.10.2024
- Lecture 6: Statistical model and machine learning (01.11.2024)
- Lecture 7: Causal inference (08.11.2024)
- Lecture 8: Lead identification and optimization (15.11.2024)
- Lecture 9: Mechanism and mode of action of drugs (22.11.2024. The lecture takes place exceptionally at Biozentrum, Hörsaal U1.141)
- Dies academicus (29.11.2024, no lecture)
- Lectuer 10: DMPK and PKPD modelling (06.12.2024)
- Lecture 11: Guest lecture (13.12.2024)
- Lecture 12: A collaboration challenge (20.12.2024)
- Further questions or suggestions?
- Offline activities
- 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. 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
Please fill the pre-course survey before attending the course.
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.
- Preparatory reading/watching
- If you need a refresher of the central dogma of biology, please watch this YouTube video.
- If you are not familiar with the process of drug discovery and development, you may benefit from watching this YouTube video made by Novartis.
- Slides of lecture 1
- Offline activities:
- Assignment: see slide #20. Please submit your response via this Google Form latest by September 25th, Thursday, End of Business Day (EOB).
- Please fill the post-lecture survey. The due date is the same as the offline activities.
Lecture 2: The What, the Who, and the How of drug discovery
In the second lecture, we discuss the workflow of modern drug discovery, the relevant stakeholders, and possible paths towards new drugs.
- Slides of lecture 2
- Offline activities
- Please fill the post-lecture survey: I look forward to your feedback!
- Assignment is described here, which is a Google Form with which you will submit your answers. Deadline: EOB October 2nd.
- Keep reading and thinking about your roles as pharma company, regulatory agency, insurance company, medical doctors, and patients, and exchanging with your fellow peers.
Lecture 3: Key questions in drug discovery
In the third lecture, we explore the five key questions in drug discovery: medical need, target and modality, PK/PD, benefit and risk, and patient stratification.
- Slides of lecture 3
- Offline activities
- Please fill the post-lecture survey: I look forward to your feedback!
- Assignment #1: If you need a quick refreshment of the concept of central dogma and the process of information flow from DNA to protein, check out this video by yourgenome (~3 min).
- Assignment #2: Watch the Nobel Prize Lecture by Katalin Karikó, Nobel Prize Laureate in Physiology or Medicine 2023 (42 min). Think about three questions: (1) What did you find most interesting? (2) What surprised you the most? (3) What you can do differently in your work and life, inspired by the learning shared by Katalin Karikó? Submit your answers via Google Form by Thursday, 09.10., EOB.
Lecture 4: Biological foundation of drug discovery
In lecture 4, we will explore biological foundations of drug discovery.
- Slides of lecture 4
- Offline activities:
- Please fill the post-lecture survey (including the survey about an Ask Me Anything session).
- Assignment #1: Read the Popular Information of Nobel Prize 2025 in Physiology or Medicine 2025. What was the most interesting learning for you?
- Assignment #2: Read the article Principles of early drug discovery by Hughes et al. (2011) twice. The first time, read the whole paper however as you wish. The second time, use one sentence to summarize each paragraph of the sections ‘target identification’ and ‘target validation’. Write down your summary sentences (no formatting/polishing needed). Submit your answers via this form by Thursday, 16.10., EOB.
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.