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Welcome to the website for Introduction to Applied Mathematics and Informatics In Drug Discovery, the course series running at the Department of Mathematics and Informatics, University of Basel in the fall semester 2020.

We focus on interdisciplinary research in drug discovery with mathematics as the language and informatics as the tool. More information on the course can be found at the course directory of the University Basel.

Table of content

Lectures with Zoom: registration required

Due to the coronavirus pandemic, the course in 2020 will take place online with Zoom.

In order to attend the interactive lectures, please register yourself once for the Zoom meetings with this link at The registration is necessary for students who want to get credits and for auditors.

Time and place

The lecture takes place on Fridays between 12:15 and 13:45 exclusively on Zoom. The meeting will be active between 12:00 and 14:00, so that any questions to the lecturer can be addressed before and after the lecture.

You can add the events to your calendar once you register for the Zoom meetings with the link above.

Course material and licensing

Both slides and board are used for the course. Course material, including lecture notes, slides, and reading material, are shared on the course’s web site,, unless otherwise specified in the course.

All Zoom sessions are recorded and distributed among attendees.

All course material, unless otherwise stated, is shared under the Creative Commons (CC-BY-SA 4.0) license.


1. Drug discovery: an overview (18.09.2020)

2. The central dogma and the discovery of Vemurafenib (25.09.2020)

3. Biological sequence analysis (02.10.2020)

4. From protein structure to screening (09.10.2020)

5. Principles of molecular modelling (16.10.2020)

6. From molecular networks to omics and cellular modelling (23.10.2020)

7. Omics and cellular modelling (II) (30.10.2020)

8. PK/PD and PBPK modelling (06.11.2020).

9. Population modelling and clinical trials (13.11.2020)

10. Guest-speaker session (I) (20.11.2020)

Nicolò Milani got his PhD at University of Perugia (Italy) in collaboration with Molecular Discovery Ltd. The main topic of his PhD was the application of in vitro data in combination with molecular modelling for drug metabolism investigation. The area of interest was mostly h-Aldehyde Oxidase and h-UGT2B10. The h-UGT2B10 project was in collaboration with Roche and he spent one year in Basel. He started his Postdoc in Roche one year ago and his project is about the quantitative application of mathematical modelling for complex in vitro systems (e.g. Organ-on-a-Chip) in DMPK.

Philipp Mekler is a biochemist, mathematician, and businessman, with more than forty years’ experience in life sciences.

11. Dies academicus - optional Ask Me Anything session (27.11.)

12. Guest-speaker session (II) (4.12)

Detlef Wolf is a computer scientist by training, with an inclination towards working at the interface between biology and informatics. For many years he worked on data management systems, initially for genomic data, later biochemical data. Detlef likes to write re-usable, configurable code that supports scientific work. In the past Detlef was a member of the Roche bioinformatics group led by Clemens Broger. Now he is a member of the data science group led by Martin Strahm. The current focus of his work are Digital Biomarkers and quantum computing.

Jenny Devenport is currently a Director of Biostatistics at Roche, overseeing a talented group of statisticians who work on late-stage products close to or just after launch in Immunology, Ophthalmology, and Neuroscience. With an unusually broad background covering public health, drug and device development and medical affairs, she champions scientific curiosity to improve patient care through the generation and effective communication of evidence.

13. Student presentation (I) (11.12.)

14. Student presentation (II) (18.12.)

Student presentation topics and reference papers

  1. Productivity and cost of drug discovery and development:
    • Dickson, Michael, and Jean Paul Gagnon. 2004. “Key Factors in the Rising Cost of New Drug Discovery and Development.” Nature Reviews Drug Discovery 3 (5): 417. (PDF) Why drug discovery and development has become more expensive over time?
    • Paul, Steven M., Daniel S. Mytelka, Christopher T. Dunwiddie, Charles C. Persinger, Bernard H. Munos, Stacy R. Lindborg, and Aaron L. Schacht. 2010. “How to Improve R&D Productivity: The Pharmaceutical Industry’s Grand Challenge.” Nature Reviews Drug Discovery 9 (3): 203–14. (PDF) How can we do better?
    • Waring, Michael J., John Arrowsmith, Andrew R. Leach, Paul D. Leeson, Sam Mandrell, Robert M. Owen, Garry Pairaudeau, et al. 2015. “An Analysis of the Attrition of Drug Candidates from Four Major Pharmaceutical Companies.” Nature Reviews Drug Discovery 14 (7): 475–86. (PDF) We learn at least as much from our failures as from our successes.
  2. Machine learning in drug discovery:
    • Vamathevan, Jessica, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, et al. 2019. “Applications of Machine Learning in Drug Discovery and Development.” Nature Reviews Drug Discovery 18 (6):463. (PDF) A recent and industry-affine review.
    • Yang, Kevin K., Zachary Wu, and Frances H. Arnold. 2019. “Machine-Learning-Guided Directed Evolution for Protein Engineering.” Nature Methods 16 (8): 687. (PDF) Interesting application of ML to protein engineering, which can be promising therapeutical and diagnostic agents.
    • McCloskey, Kevin, Eric A. Sigel, Steven Kearnes, Ling Xue, Xia Tian, Dennis Moccia, Diana Gikunju, et al. 2020. “Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding.” Journal of Medicinal Chemistry, June. (PDF) Interesting paper by Google and X-Chem to use ML explore DELT screening data.
  3. Can machine learning improve the productivity of drug discovery? If yes, how?:
    • There are much enthusiasm and hope. Besides the papers listed above, also see a blog post by Garvin Edwards at AstraZeneca, who is working on using knowledge graphs for target identification (and probably more), for an example of applying machine learning in preclinical research.
    • See the following paper for discussions that are relevant for clinical development: Shah, Pratik, Francis Kendall, Sean Khozin, Ryan Goosen, Jianying Hu, Jason Laramie, Michael Ringel, and Nicholas Schork. 2019. “Artificial Intelligence and Machine Learning in Clinical Development: A Translational Perspective.” Npj Digital Medicine 2 (1): 1–5.
    • Precautions are warranted: machine learning has its own limitations, especially if our goal is to increase the productivity of drug discovery. See a post by Derek Lowe (the author of the blog In The Pipeline), AI, Machine Learning, and the pandemic, and linked blog posts therein, for inspiration for critical thoughts.
  4. Drug discovery for SARS-Cov-2 (COVID-19):
  5. How drugs are discovered: target-based and phenotypic screening:
    • Kleinstreuer, Nicole C., Jian Yang, Ellen L. Berg, Thomas B. Knudsen, Ann M. Richard, Matthew T. Martin, David M. Reif, et al. 2014. “Phenotypic Screening of the ToxCast Chemical Library to Classify Toxic and Therapeutic Mechanisms.” Nature Biotechnology 32 (6): 583–91. (PDF). Comments by David: An example of phenotypic screening, though not directly for drug discovery but rather for mechanism and safety understanding, nevertheless interesting.
    • 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) Current and relevant review from the industrial point of view.
    • Swinney, David C., and Jason Anthony. 2011. “How Were New Medicines Discovered?” Nature Reviews Drug Discovery 10 (7): 507–19. (PDF) Analysis of historical data reveals how new drugs are discovered.
  6. Mathematical and computational modelling in biology and drug discovery:
    • Allen, Richard, and Helen Moore. 2019. “Perspectives on the Role of Mathematics in Drug Discovery and Development.” Bulletin of Mathematical Biology, January, 1–11. (PDF) If you major in mathematics and considers drug discovery and development as a career option, read this.
    • Turing, Alan Mathison. 1952. “The Chemical Basis of Morphogenesis.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 237 (641): 37–72. (PDF) The foundation paper of mathematical biology. Intellectual, beautiful, and illuminating.
    • Tuszynski, Jack A, Philip Winter, Diana White, Chih-Yuan Tseng, Kamlesh K Sahu, Francesco Gentile, Ivana Spasevska, et al. 2014. “Mathematical and Computational Modeling in Biology at Multiple Scales.” Theoretical Biology & Medical Modelling 11 (December). (PDF) A broad review of mathematical and computational modelling approaches across scales in biology.

End-term project

I expect every participant to choose one concept from the list below and to write a short essay (1000-2500 words) introducing the concept for non-experts, with examples and ideally applications in drug discovery.

List of candidate topics:

  1. Statistical power and sample size calculation
  2. Information (Shannon) entropy
  3. Principal component analysis (PCA)
  4. Non-negative matrix factorization
  5. The Viterbi algorithm
  6. MCMC (Markov-Chain Monte Carlo)
  7. The EM (Expectation-Maximization) algorithm
  8. Agent-based modelling
  9. Gaussian process
  10. Bayesian networks

The preference is to be submitted by a Google Form (sent out via emails) by December the 10th, Thursday. The deadline for submitting the essay is January the 15th, 2021, Friday. No extension is possible.


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