Jitao David Zhang
- Course title: Applied Mathematics and Informatics in Drug Discovery (AMIDD)
- Semester: Every autumn Semester starting from 2019
- Course frequency: every autumn semester
- Lecturer: Jitao David Zhang (jitao-david.zhang@unibas.ch)
- Weblink: http://www.amidd.ch
- Bibliography: Lecture notes and slides. Recommend reading and media will be distributed.
Applied mathematics and informatics is indispensable in modern drug discovery to enable decisions that have direct impacts on lives. This introductory course will offer a practitioner's review of mathematical concepts, informatics tools, and industrial approaches in relevant fields, especially bioinformatics, molecular modelling, cheminformatics, mathematical modelling, experiment design and statistical inference, and machine learning. It is hoped that the students are exposed to the interdisciplinary and multiscale modelling nature of drug discovery, and are motivated to deepen their knowledge in relevant fields in future study and practice, to be able to solve open challenges in drug discovery.
- We explore the drug-discovery process and study applications of mathematics and informatics with case studies.
- We examine how mathematics concepts and informatics tools are used to model complex systems at multiple levels, and how the multiscale modelling approach contributes to drug discovery.
- We encourage interdisciplinary thinking, application- and question-driven learning, and teamwork.
- Language of instruction: English
- Course auditors welcome: yes
Any students interested in applied mathematics and informatics can participate. Students with background in biology, chemistry, and pharmacy are also welcome if they are interested in quantitative aspects of drug discovery.
Though no prerequisite courses are obligatory, elementary understanding of statistics, probability, calculus, and ordinary differential equations are helpful. High-school knowledge in physics, chemistry, and biology are required. Knowledge and proficiency in at least one programming language (preferably C/C++, Java, R, Python, or Julia) is very helpful to try real-world problems.
- Applied mathematics
- Applications and Related Topics (Computer Science)
- Life-science informatics
Scores will be given in scale 1-6 by 0.5, by participation (20%), near-end-term presentation (30%), and end-term oral examination (50%).
- Drug discovery: an overview
- Biological sequence analysis
- Protein structure and function
- Chemical structure representation and search
- Molecular interaction and modelling
- Omics: genomics, transcriptomics, and proteomics
- PK/PD and PBPK modelling
- Clinical trials and population modelling
- Bayesian modelling, machine learning, and causal inference
- Dies Academicus - optional AMA session
- Guest speakers (to be announced)
- Student presentation (I)
- Student presentation (II)
The course is designed to be part of the curriculum of applied mathematics for undergraduate and master students to give a high-level overview of applications of mathematics and informatics tools in drug discovery. Therefore, while key concepts and principles are introduced, almost all subjects can only be briefly and likely superficially touched. Intensive investigations into some subjects, for instance bioinformatics and computational biology, are planned as follow-up courses.
The course consists of element sessions (90 minutes each), a near-end-term presentation, and an final oral examination. Besides an introduction to drug discovery, the sessions will cover (1) bioinformatics and computational biology, (2) cheminformatics and computer-aided drug design, (3) mathematical modelling, and (4) statistics and machine learning. The sessions are so arranged that they roughly reflect the linear model of the drug discovery process, including target assessment, screening, lead identification and optimization, preclinical safety evaluation, PK/PD modelling and clinical trials prior to filing and regulatory approval.
I got great input and support from numerous colleagues to design and implement the course. Especially I would like to thank
- Martin Ebeling, Fabian Birzele, and Iakov Davydov for suggestions and help with the bioinformatics part.
- Lisa Sach-Peltason, Christian Kramer and Michael Reutlinger for teaching me a lot about cheminformatics and drug design.
- Manfred Kansy, Holger Fischer, and Matthias Nettekoven for leading me into the field of medicinal chemistry and pharmacology.
- Arne Rufer, Ken Wang, Norman Mazer, Neil Parrot Jones, Francois Mercier, Benjamin Ribba, Hans-Peter Grimm, and Nicolas Frey for selfless sharing of knowledge in mathematical modelling, PK/PD modelling, and clinical pharmacology and pharmacometrics.
- Prof. Markus von Kienlin, Andreas Bruns, Gonzalo Christian Duran Pacheco, Stanley Lazic, Kasper Rufibach for statistics, biomarker and clinical development.
I want to thank Prof. Gianluca Crippa, Prof. Helmut Harbrecht , Prof. Jiří Černý, Dr. Jung Kyu Canci, Prof. Enno Lenzmann, and other professors for the great support for me to offer this course at the Department of Mathematics and Informatics, University of Basel. Dr. Philipp Mekler, Gang Mu, Prof. Dr. Niko Beerenwinkel, and other colleagues provided great support and help to shape the lectures. Heidi Karypidis and Barbara Fridez provided outstanding administrative support.
Students that attended previous series have provided invaluable feedback to improve and enrich the course. I learned a lot about mathematics, informatics, and knowledge sharing from them. I am thankful to be able to learn together with them.
I dedicate the lecture series to Clemens Broger, a great mentor of mine and of many other colleagues, who stayed curious, courageous, passionate, and true to himself until the last day of his life.
The course series was held in autumn semester 2019 at the University of Basel. See archive link for archived content of the first course.
This document and the course material are published under the Creative Commons Attribution-ShareAlike 4.0 International License. They were prepared and accomplished by Jitao David Zhang in his personal capacity. The opinions expressed in this document and the course material are author's own and do not reflect the view of F. Hoffmann-La Roche Ltd.