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.
A one-page summary of the course 2026 can be found here AMIDD 2026 Agenda. 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 (18.09.2026)
- Lecture 2: The linear model of drug discovery (25.09.2026)
- Lecture 3: The What, the Who, and the How of drug discovery (02.10.2026)
- Lecture 4: Biological foundation of drug discovery (09.10.2026)
- Lecture 5: Protein as drug target (16.10.2026)
- Lecture 6: Statistical, machine learning, and artificial intelligence models (23.10.2026)
- Lecture 7: Causal inference (30.10.2026)
- No lecture on 06.11.26 (06.11.2026, no lecture)
- Lecture 8: Lead identification and optimization (13.11.2026)
- Lecture 9: Mechanism and mode of action of drugs (20.11.2026, Hörsaal 101 in der Alten Universität)
- No lecture on 27.11.26 due to Dies academicus (27.11.2026, no lecture)
- Lecture 10: PK/PD modeling and basics of clinical trials (04.12.2026)
- Lecture 11: Guest lectures (11.12.2026)
- Lecture 12: A collaboration challenge (18.12.2026)
- Commonly Asked Questions and Answers
- 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. 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 (taking place randomly through out the semester, 30%), offline activities (announced in each lecture, 40%), and a collaboration challenge in the final session (30%).
Here you can have an overview of the records of submitted offline activities.
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
- Please fill the short post-lecture survey using the Google Form of the post-lecture survey of Lecture 1. Please submit your response latest by September 24th, Thursday, End of Business Day (EOB).
- Offline activities: see slide #29. Please submit your response at the Google Form of the offline activities of Lecture 1 latest by September 24th, Thursday, EOB.
Lecture 2: The linear model of drug discovery
In the second lecture, we planned to discuss the workflow of modern drug discovery, the relevant stakeholders, and possible paths towards new drugs. Finally we managed to discuss the linear model of drug discovery, different stages, and the time, cost, and technical success rates associated with each phase.
- Slides of lecture 2
- Offline activities
- Please fill the survey of lecture 2: I look forward to your feedback!
- Read two related publications, one on the principles of early drug discovery by Hughes et al. (2010), and the other on the productivity challenge of pharma industry. Despite that the papers are published many years ago, most key points have withstood the testing of time. Please fill out the offline activity form of lecture 2 by October 1st, EOB.
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 anonymous survey for the third lecture.
- See the task in the page #18 of the slides. Use this Google Form to submit your replies.
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 [Form URL to be updated].
- 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 [Form URL to be updated] by Thursday, 15.10., 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 [Form URL to be updated].
- 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 form [Form URL to be updated]. Deadline: Thursday before Lecture 6.
Lecture 6: Statistical, machine learning, and artificial intelligence models
In lecture 6, we will explore statistical, machine learning (ML), and artificial intelligence (AI) models and their applications in drug discovery.
- Slides of lecture 6
- Offline activities:
- Please fill the post-lecture survey [Form URL to be updated].
- 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 [Form URL to be updated]. 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
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 [Form URL to be updated].
- 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 [Form URL to be updated]. Submission deadline: Thursday, November the 5th, EOB.
No lecture on 06.11.26
We will have no lecture on 06.11.26 because of a conflicting meeting. We will use the time for a hand-on project with tabular models. More details will follow.
- Activities
- Read Accurate predictions 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 form [Form URL to be updated]. Deadline: Thursday, November the 12th, EOB.
Lecture 8: Lead identification and optimization
In lecture 8, 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 8
- Offline activities
- Please fill this form [Form URL to be updated] 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 [Form URL to be updated] until November 19th, 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 9: Mechanism and mode of action of drugs
Attention: this lecture takes place exceptionally at the Hörsaal 101 in the Alte Universität, Rheinsprung 9, 4051 Basel.
- Slides of lecture 9
- Offline activities
- Please fill out a survey [Form URL to be updated] 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 [Form URL to be updated] until December the 3rd.
AND: NO LECTURE on Dies academicus on November 27th, 2026.
Lecture 10: PK/PD modeling and basics of clinical trials
In lecture 10, we will introduce PK/PD modelling and basic concepts in clinical trials.
- Slides of lecture 10
- Offline activities: pick one publication to read, depending on your interest.
- [Introduction to PBPK modelling] Jones, H. M., and K. Rowland‐Yeo. Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development. CPT: Pharmacometrics & Systems Pharmacology (2013)
- [Application of machine learning for PK prediction] Komissarov et al. Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage. Molecular Pharmaceutics (2024).
- Confirm the completion of the task, and bring up any questions, [Form URL to be updated] until EOB December 10th.
Lecture 11: Guest lectures
Details to be announced.
Lecture 12: A collaboration challenge
Commonly asked questions and answers
- What happens if I have to miss lectures? If you are missing courses due to reasons beyond your control (e.g. sickness, military service, etc.), please submit a written confirmation either before or latest 14 days after. Without the confirmation you will not receive the grade for the quiz that may take place during the course.
Further questions or suggestions?
Please contact the lecturer, Jitao David Zhang, at jitao-david.zhang@unibas.ch.