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 2021.

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

Lectures with Zoom

Due to the coronavirus pandemic, the course in 2021 will take place online with Zoom exclusively. The link to join the Zoom meeting is: https://unibas.zoom.us/j/68803401669. The passcode for the Zoom meeting is shared among registered students via emails.

Time and place

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

Course material and licensing

Course material, including lecture notes, slides, and reading material, are shared on the course’s web site, AMIDD.ch, 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.

Pre-course survey

Prior to attending the first session, please fill out the voluntary pre-course survey. It helps me to shape the course to meet your needs.

Syllabus

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. Omics and cellular modelling

9. PKPD modelling

10. Dies academicus - optional Ask Me Anything session

You can ask me anything in this session, which will be exceptionally not recorded.

Besides scientific topics in drug discovery, my experience is that many students are interested in career topics: should I do a PhD or not? Should I consider working in industry? You may this article below interesting if you consider doing a PhD and perhaps doing a postdoc in pharma: Zhang, Jitao David. “Ten Simple Rules for Doing a Postdoc in Pharma.” PLOS Computational Biology 17, no. 6 (June 3, 2021): e1008989.

11. Guest-speaker session

This year we will welcome our guest speakers Dr. Elif Ozkirimli, Dr. Thomas Sander, and Dr. Juliane Siebourg-Polster to share with us their industrial research experience in applied mathematics and informatics in drug discovery.

Please note that the course starts exceptionally at 12:05.

Student presentation topics and reference papers

You can vote for your presentation topics via Google Form by November the first.

In HS 2021, our class will present on four topics: machine learning, causal inference, drug discovery for SARS-Cov-2, and mathematical and computational modelling. Each team is expected to deliver a talk of 30 minutes, which is followed by 10 minutes of Q&A.

Please find a table here listing teams, the presentation topics, and the time slots in the PDF format (updated on 14.11.2021).

Unfortunately we cannot satisfy the availability of everyone. I hope you can understand the difficulty, and arrange within each team so that all members can contribute to the presentation, whether she or he is available during the presentation is not.

Below we list the topics and relevant references. Each group is expected to select 2-4 references from the given ones and discuss them. Additional references are welcome.

12. Student presentation (I)

Machine learning and artificial intelligence in drug discovery, presented by the Turing team

Reference papers:

  1. Bender, Andreas, and Isidro Cortés-Ciriano. “Artificial Intelligence in Drug Discovery: What Is Realistic, What Are Illusions? Part 1: Ways to Make an Impact, and Why We Are Not There Yet.” Drug Discovery Today, December 17, 2020. https://doi.org/10.1016/j.drudis.2020.12.009.
  2. Bender, Andreas, and Isidro Cortes-Ciriano. “Artificial Intelligence in Drug Discovery: What Is Realistic, What Are Illusions? Part 2: A Discussion of Chemical and Biological Data.” Drug Discovery Today 26, no. 4 (April 1, 2021): 1040–52. https://doi.org/10.1016/j.drudis.2020.11.037.
  3. Barredo Arrieta, Alejandro, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, et al. “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.” Information Fusion 58 (June 1, 2020): 82–115. https://doi.org/10.1016/j.inffus.2019.12.012.
  4. Stokes, Jonathan M., Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, et al. “A Deep Learning Approach to Antibiotic Discovery.” Cell 180, no. 4 (February 20, 2020): 688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021.
  5. Basile, Anna O., Alexandre Yahi, and Nicholas P. Tatonetti. “Artificial Intelligence for Drug Toxicity and Safety.” Trends in Pharmacological Sciences 40, no. 9 (September 1, 2019): 624–35. https://doi.org/10.1016/j.tips.2019.07.005.
  6. Ietswaart, Robert, Seda Arat, Amanda X. Chen, Saman Farahmand, Bumjun Kim, William DuMouchel, Duncan Armstrong, Alexander Fekete, Jeffrey J. Sutherland, and Laszlo Urban. “Machine Learning Guided Association of Adverse Drug Reactions with in Vitro Target-Based Pharmacology.” EBioMedicine, June 18, 2020, 102837. https://doi.org/10.1016/j.ebiom.2020.102837.

Causal inference in disease understanding and drug discovery, presented by the Hill team

Reference papers and software:

  1. Hill, Austin Bradford. “The Environment and Disease: Association or Causation?” Proceedings of the Royal Society of Medicine 58, no. 5 (May 1965): 295–300.
  2. Shrier, Ian, and Robert W. Platt. “Reducing Bias through Directed Acyclic Graphs.” BMC Medical Research Methodology 8, no. 1 (October 30, 2008): 70. https://doi.org/10.1186/1471-2288-8-70.
  3. Evans, David M., and George Davey Smith. “Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality.” Annual Review of Genomics and Human Genetics 16, no. 1 (2015): 327–50. https://doi.org/10.1146/annurev-genom-090314-050016.
  4. Prosperi, Mattia, Yi Guo, Matt Sperrin, James S. Koopman, Jae S. Min, Xing He, Shannan Rich, Mo Wang, Iain E. Buchan, and Jiang Bian. “Causal Inference and Counterfactual Prediction in Machine Learning for Actionable Healthcare.” Nature Machine Intelligence 2, no. 7 (July 2020): 369–75. https://doi.org/10.1038/s42256-020-0197-y.
  5. Bica, Ioana, Ahmed M. Alaa, Craig Lambert, and Mihaela van der Schaar. “From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges.” Clinical Pharmacology & Therapeutics 109, no. 1 (2021): 87–100. https://doi.org/10.1002/cpt.1907.
  6. Tutorial of DoWhy.

13. Student presentation (II)

Drug-discovery for SARS-Cov-2, presented by the Almeida team

  1. Tummino, Tia A., Veronica V. Rezelj, Benoit Fischer, Audrey Fischer, Matthew J. O’Meara, Blandine Monel, Thomas Vallet, et al. “Drug-Induced Phospholipidosis Confounds Drug Repurposing for SARS-CoV-2.” Science 373, no. 6554 (July 30, 2021): 541–47. https://doi.org/10.1126/science.abi4708.
  2. Delft, Frank von, Mark Calmiano, John Chodera, Ed Griffen, Alpha Lee, Nir London, Tatiana Matviuk, Ben Perry, Matt Robinson, and Annette von Delft. “A White-Knuckle Ride of Open COVID Drug Discovery.” Nature 594, no. 7863 (June 2021): 330–32. https://doi.org/10.1038/d41586-021-01571-1.
  3. Mei, Miao, and Xu Tan. “Current Strategies of Antiviral Drug Discovery for COVID-19.” Frontiers in Molecular Biosciences 8 (2021): 310. https://doi.org/10.3389/fmolb.2021.671263.
  4. Kwok, Andrew J., Alex Mentzer, and Julian C. Knight. “Host Genetics and Infectious Disease: New Tools, Insights and Translational Opportunities.” Nature Reviews Genetics 22, no. 3 (March 2021): 137–53. https://doi.org/10.1038/s41576-020-00297-6.
  5. Heinz, Franz X., and Karin Stiasny. “Distinguishing Features of Current COVID-19 Vaccines: Knowns and Unknowns of Antigen Presentation and Modes of Action.” Npj Vaccines 6, no. 1 (August 16, 2021): 1–13. https://doi.org/10.1038/s41541-021-00369-6.
  6. Relevant posts in In The Pipeline, for instance Pfizer’s Good News Is the World’s Good News and Covid Therapeutic Trial Recruitment.

Mathematical and computational modelling, presented by the Euler team

  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. https://doi.org/10.1038/s41577-019-0235-3.
  2. Jones, H. M., and K. Rowland‐Yeo. “Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development.” CPT: Pharmacometrics & Systems Pharmacology 2, no. 8 (August 1, 2013): 63. https://doi.org/10.1038/psp.2013.41.
  3. 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. https://doi.org/10.1007/BF03190058.
  4. Sancho-Araiz, Aymara, Victor Mangas-Sanjuan, and Iñaki F. Trocóniz. “The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives.” Pharmaceutics 13, no. 7 (July 2, 2021): 1016. https://doi.org/10.3390/pharmaceutics13071016.
  5. 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. https://doi.org/10.1002/psp4.12467.
  6. Lu, James, Brendan Bender, Jin Y. Jin, and Yuanfang Guan. “Deep Learning Prediction of Patient Response Time Course from Early Data via Neural-Pharmacokinetic/Pharmacodynamic Modelling.” Nature Machine Intelligence 3, no. 8 (August 2021): 696–704. https://doi.org/10.1038/s42256-021-00357-4.

End-term project

All participants are expected to finish the end-term project either individually or in a team of two people. She or he or the team shall choose one concept form the list below, upon which they will write a short essay introducing the concept to non-experts, with examples and ideally applications in drug discovery.

The essay should have an abstract (fewer 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 welcome. For such essays the source code does not count to the total words.

List of candidate topics:

  1. [Simple/Statistics] Statistical power and sample-size calculation
  2. [Simple/Machine learning] Principal component analysis (PCA)
  3. [Simple/Algorithm] The Viterbi algorithm
  4. [Simple/Mathematical modelling] The Lotka-Volterra predator-prey model
  5. [Moderate/Statistics] Exploratory factor analysis
  6. [Moderate/Machine learning] Classification and regression trees
  7. [Moderate/Causal inference] Directed acyclic graphs (DAGs) for causal inference
  8. [Challenging/Causal inference] Mendelian randomization
  9. [Challenging/Algorithm] Community detection with Leiden clustering
  10. [Challenging/Statistics] Simpson’s paradox and mixed models

The choice of topic and the team composition is to be submitted via this Google Form of voting for the project topic by Friday, December the 3rd. Two people working together need to vote just once. The deadline for submitting the essay by email is January 14th, 2022. No extension is possible.

You can view the topics of choice here

Once the essays are submitted, they will be shared with the whole class for open review and joint learning.

Assessment

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 jitao-david.zhang@unibas.ch.

Archives of past courses