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 (19.09.2025)
  • Lecture 2: The What, the Who, and the How of drug discovery (26.09.2025)
  • Lecture 3: Key questions in drug discovery (03.10.2025)
  • Lecture 4: Biological foundation of drug discovery (10.10.2025)
  • Lecture 5: Protein as drug target (17.10.2025)
  • Lecture 6: Statistical model and machine learning (24.10.2025)
  • Lecture 7: Causal inference (I) (31.10.2025)
  • Lecture 8: Causal inference (II) (7.11.2025)
  • Lecture 9: Lead identification and optimization (14.11.2025)
  • Lectuer 10: Mechanism and mode of action of drugs (21.11.2025, IMPORTANT taking place exceptionally at Hörsaal 3.10, Physical Chemistry, Klingelbergstrasse 80)
  • Dies academicus (28.11.2025, no lecture)
  • Lectuer 11: PK/PD modeling and early development (5.12.2025)
  • Lecture 12: Guest lecture (12.12.2025)
  • Lecture 13: A collaboration challenge (19.12.2025)
• 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 as drug target

In lecture 5, we will explore properties of proteins as drug targets, and learn an example of physics-based/mechanistic mathematical modelling.

• Slides of lecture 5
• Offline activities:
  • Please fill the post-lecture survey (including a follow-up survey about an Ask Me Anything session).
  • Answer the question listed in the slide #21. And choose one task below to perform. In each case, consider using the Feynman technique.
    • If you have a strong background in mathematics/statistics and particularly in machine learning, please review the material shared in the slides about proteins so that you gain more knowledge about drug targets.
    • If you have a strong background in biology but yet to develop more skills in machine learning, please read An Introduction to Machine Learning.
    • If you have strong background in both fields, please read Accurate structure prediction of biomolecular interactions with AlphaFold3, by Abramson et al.
  • Confirm that you have performed one of the tasks above by filling out this Google Form. Deadline: Thursday before Lecture 6.

Lecture 6: Statistical model and machine learning

In lecture 6, we will explore statistical and machine-learning based models.

• Slides of lecture 6
• Offline activities:
  • Please fill the post-lecture survey.
  • Read The Environment and Disease: Association or Causation? by Austin Bradford Hill (1965).
  • Read Exposure to sugar rationing in the first 1000 days of life protected against chronic disease by Gracner, Boone and Gertler (Science, 2024). Use the example to check whether the evidences and conclusion meets Hill’s criteria of causality, including (1) strength, (2) consistency, (3) specificity, (4) temporality, (5) biological gradient, (6) plausibility, (7) coherence, (8) experiment, and (9) analogy.
  • Submit the answers to the form for the offline activity of lecture 6. Deadline: EOB, Thursday before lecture 7.
  • (Optional) If you are intrigued by the findings of the study, and/or if you are interested the hypothesis of fetal origins of disease in the cardiovascular domain, please read Exposure to sugar rationing in first 1000 days after conception and long term cardiovascular outcomes: natural experiment study by Zheng et al. (BMJ, 2025).

Lecture 7: Causal inference (I)

In lecture 7, we will explore the concept of causality and application in data analysis.

• Slides of lecture 7 and 8 (until slide 16)
• Offline activities
  • Please fill the post-lecture survey.
  • Offline activities:
    • Review the slides to make sure that you understand the idea of using generative models to simulate and explore causality.
    • Read the review Causal inference in drug discovery and development.
    • (Optional) Checkout Causal inference for drug discovery and development, an accompanying repo of Rmarkdown and Python notebooks that introduce basic concepts of causal inference.
    • Answer questions in this form. Submission deadline: Thursday, November the 6th, EOB.

Lecture 8: Causal inference (II)

In lecture 8, we demonstrate the difference between correlation and causality with real-world examples, and introduce techniques to infer causality.

• Slides of lecture 7 and 8
• Offline activities
  • Please fill this form to give feedback to the lecture.
  • Read Accurate prdictions on small data with a tabular foundational model by Hollmann et al., Nature 2025.
  • Try TabPFN with any problem of your choice.
  • Share your learnings and experience by filling this Google Form. Deadline: Thursday, November the 13th, EOB.

Lecture 9: Lead identification and optimization

In lecture 9, we will have a high-level overview of the process of lead identification and optimization (LI/LO) of small-molecule drug discovery.

• Slides of lecture 9
• Offline activities
  • Please fill this form to give feedback to the lecture.
  • Mandatory: read Evaluation of the Biological Activity of Compounds: Techniques and Mechanism of Action Studies by Iain G. Dougall and John Unitt, chapter two of the book The Practice of Medicinal Chemistry. Use this and other resources, including Wikipedia and large language models (LLMs), to answer questions in your own words. Submit the answers here via Google Form until November 20th, EOB.
    • What is surface plasmon resonance (SPR), and how it is used to study the kinetics of target-ligand interactions?
    • What do $K_d$, $k_{on}$, and $k_{off}$ mean in the context of kinetics of target-ligand interaction?
    • What does microsomal clearance mean? Why it is important to measure it in drug discovery?
    • Why is plasma protein binding an important parameter for drug discovery?
    • What does GSH adduct mean? Why GSH assay is required for drug candidates?
    • What is hERG, and what does hERG assay measure?
    • What is Ames test, and what does it measure?
    • What is the micronucleus test, and why it is important to perform it?
    • What does phototoxicity mean, and why it is important to predict or measure it?
  • Optional: In the lecture, we dissected the example of the discovery of a novel, reversible, and specific MAGL inhibitor. If you are interested in exploring the topic further, read the original publication Structure-Guided Discovery of cis-Hexahydro-pyrido-oxazinones as Reversible, Drug-like Monoacylglycerol Lipase Inhibitors by Bernd Kuhn, et al. (J Med Chem, 2024).

An unsolicited advice about the readings: don’t get frustrated if you meet details that you do not understand at the first sight. That is normal when reading interdisciplinary papers. It may help to focus on the big lecture.

Lecture 10: Mechanism and mode of action of drug candidates

• Slides of lecture 10
• Offline activities
  • Please fill out a two-question survey to give feedback about the lecture.
  • Read Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications by Shih et al., Nature Reviews Drug Discovery (2018). Submit your learnings here via Google Form until December the 4th.

AND: NO LECTURE on Dies academicus on November 28th, 2025.

Lecture 11: 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

• AMIDD 2024
• AMIDD 2023
• AMIDD 2022
• AMIDD 2021
• AMIDD 2020
• AMIDD 2019