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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 2022.

The course series introduces interdisciplinary research in drug discovery with mathematics as the language and informatics 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

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,, 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

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.


1. Drug discovery: an overview

2. The central dogma and drug discovery

3. Biological sequence analysis

4. From sequences to structures

5. Proteins and ligands

6. Structure- and ligand-based drug design

7. From individual interactions to networks

8. Biological networks

9. Omics and cellular modelling

10. Dies academicus

No lectures. We invite registered students to visit Roche. Details are announced via email.

11. PK/PD modelling

12. Guest-speaker session

To be announced

Student presentation topics and reference papers

Each team is expected to deliver a talk of 30 minutes, which is followed by 10 minutes of Q&A.

You can vote for your presentation topics via the Google Form of voting for presentation topics by November 14th.

In HS 2022, our class will present on the following four topics:

  1. Phenotypic drug discovery: how phenotypic drug discovery complements target-based drug discovery.
  2. Productivity in drug discovery: return of investment, cost, and research productivity in drug discovery.
  3. Mathematical modeling in drug discovery: mathematical modeling techniques used in preclinical and clinical studies.
  4. Reverse translation: how we can leverage data from clinical trials and observational studies to empower preclinical studies.

13. Student presentation (I)

Team 1: Phenotypic drug discovery (December 16th)

  1. Vincent, Fabien, Arsenio Nueda, Jonathan Lee, Monica Schenone, Marco Prunotto, and Mark Mercola. “Phenotypic Drug Discovery: Recent Successes, Lessons Learned and New Directions.” Nature Reviews Drug Discovery, May 30, 2022, 1–16.
  2. Moffat, John G., Fabien Vincent, Jonathan A. Lee, Jörg Eder, and Marco Prunotto. 2017. “Opportunities and Challenges in Phenotypic Drug Discovery: An Industry Perspective.” Nature Reviews Drug Discovery 16 (8): 531–43. (PDF)
  3. Vincent, Fabien, Paula M. Loria, Andrea D. Weston, Claire M. Steppan, Regis Doyonnas, Yue-Ming Wang, Kristin L. Rockwell, and Marie-Claire Peakman. “Hit Triage and Validation in Phenotypic Screening: Considerations and Strategies.” Cell Chemical Biology 27, no. 11 (November 19, 2020): 1332–46.

Team 2: Productivity in drug discovery and development (December 16th)

  1. Smietana, Katarzyna, Leeland Ekstrom, Barbara Jeffery, and Martin Møller. “Improving R&D Productivity.” Nature Reviews Drug Discovery 14, no. 7 (July 2015): 455–56.
  2. Ringel, Michael S., Jack W. Scannell, Mathias Baedeker, and Ulrik Schulze. “Breaking Eroom’s Law.” Nature Reviews Drug Discovery 19, no. 12 (April 16, 2020): 833–34.
  3. Fernando, Kathy, Sandeep Menon, Kathrin Jansen, Prakash Naik, Gianluca Nucci, John Roberts, Shuang Sarah Wu, and Mikael Dolsten. “Achieving End-to-End Success in the Clinic: Pfizer’s Learnings on R&D Productivity.” Drug Discovery Today, December 2021, S1359644621005444.
  4. Scannell, Jack W., James Bosley, John A. Hickman, Gerard R. Dawson, Hubert Truebel, Guilherme S. Ferreira, Duncan Richards, and J. Mark Treherne. “Predictive Validity in Drug Discovery: What It Is, Why It Matters and How to Improve It.” Nature Reviews Drug Discovery, October 4, 2022, 1–17.

14. Student presentation (II)

Team 3: Mathematical modelling in drug discovery (December 23rd)

  1. Handel, Andreas, Nicole L. La Gruta, and Paul G. Thomas. “Simulation Modelling for Immunologists.” Nature Reviews Immunology 20, no. 3 (March 2020): 186–95.
  2. Gieschke, R., and J. L. Steimer. “Pharmacometrics: Modelling and Simulation Tools to Improve Decision Making in Clinical Drug Development.” European Journal of Drug Metabolism and Pharmacokinetics 25, no. 1 (March 2000): 49–58.
  3. Elmokadem, Ahmed, Matthew M. Riggs, and Kyle T. Baron. “Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling With Mrgsolve: A Hands-On Tutorial.” CPT: Pharmacometrics & Systems Pharmacology 8, no. 12 (2019): 883–93.

Team 4: Reverse translation (December 23rd)

  1. Kasichayanula, Sreeneeranj, and Karthik Venkatakrishnan. “Reverse Translation: The Art of Cyclical Learning.” Clinical Pharmacology & Therapeutics 103, no. 2 (February 1, 2018): 152–59.
  2. Maciejewski, Mateusz, Eugen Lounkine, Steven Whitebread, Pierre Farmer, William DuMouchel, Brian K Shoichet, and Laszlo Urban. “Reverse Translation of Adverse Event Reports Paves the Way for De-Risking Preclinical off-Targets.” Edited by Fiona M Watt. ELife 6 (August 8, 2017): e25818.
  3. Michoel, Tom and Zhang, Jitao David. “Causal inference in drug discovery and development”.

End-term project

The choice of topic and the team composition is to be submitted via this Google Form of voting for the project topic by Thursday, December the 1st. Two people working together need to vote just once. The deadline for submitting the essay by email is January 13th, 2022. No extension is possible.

All participants are expected to finish the end-term project in a team of two people, which is preferred, or individually. She or he or the team shall choose one concept from a list of candidate topics, upon which they will write a short essay introducing the concept to non-experts, with examples and ideally applications in drug discovery. The topics will be announced during the semester.

List of candidate topics of year 2022:

The essay should have an abstract (less than 200 words), a list of references, and a main text that does not exceed 3,000 words. Visual elements like table and figures that help readers understand the concept are welcome but not obligatory. In case of a team work, the contribution of the two people should be specified.

Essays in literature-programming styles, combining texts and source code with Jupyter notebook or Rmarkdown, are encouraged. For such essays the source code does not count to the total words.

Submitted essays will be shared with the whole class for open review and joint learning.


The final note is given by participation (40%), presentation (30%), and project work (30%).

Further questions or suggestions?

Please contact the lecturer, Jitao David Zhang, at

Archives of past courses