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Mathematical and Computational Biology In Drug Discovery

University of Basel/ Spring Semester 2024/ Fridays 12:15-14:00

Welcome to the web page for Mathematical and Computational Biology in Drug Discovery, the course series running at the Department of Mathematics and Computer Science, University of Basel in the spring semester 2024.

The course is open to all students who wish to learn about principles and techniques of mathematical and computational biology as well as their applications in drug discovery.

Find administrative details about the lecture in the course directory of University of Basel.

Table of content


To prepare for the course, please (1) check that you have acquired required prior knowledge, (2) make sure that you can commit enough time to the course, (3) note that lectures will be in person, and (4) fill the pre-course survey.

You should foremost be familiar with the content covered by the course series Introduction to Applied Mathematics and Informatics In Drug Discovery that run in fall semesters.

The MCBDD course on average requires about 2-3 hours’ time each week for reading assignments or programming tasks.



Lectures take place on Fridays between 12:15 and 14:00 in Seminarraum 05.002 in Spiegelgasse 5, near Schifflände, 4070 Basel. See Syllabus for the topics we plan to cover.

Topics that we shall discuss

We mainly discuss following topics from biology

We mainly discuss applications of following mathematical and computational topics:

Course material and licensing

Course material, including lecture notes, slides, and reading material, is shared on the course’s web site,, under the Creative Commons Attribution-ShareAlike 4.0 Interactional License unless otherwise specified.


The final grade is given by participation (50%) and offline activities (50%).


Module Zero: Introduction

Module Zero is an introduction to mathematical and computational biology in drug discovery.

Module I: What are drug targets and where to find them?

This module consists of two lectures: (1) what makes a good drug target , and (2) how to identify, assess, and validate drug targets?

Prior to attending the courses, you can refresh your knowledge in the central dogma of molecular biology and in the human genome by watching the animation film From DNA to protein - 3D by yourgenome, and the film mRNA processing and the spliceosome by WEHI that combines an artist’s impression and simulation.

Module II: What can we do if there are no good targets?

Module II discusses about alternatives to target-based drug discovery, in particular phenotypic drug discovery. It includes two lectures: (1) phenotypic screening with chemogenomic libraries, and (2) molecular phenotypic screening based on gene expression.

Module III: What kind of drug should we develop?

Module III considers modality selection from a computational point of view. It includes two lectures: (1) novel small molecules and antisense oligonucleotides, and (2) antibodies, multi-target drugs, and gene- and cell-therapies.

Please submit your answers, together with your code on GitHub or other software repo, to this Google Form for Module III offline activity. Please finish the assignment by May 31st, Friday, in order to get the credit.

Module IV: What efficacy and safety profiles can we expect?

Module IV focuses on MoA inference for safety and efficacy profiles of drug candidates. We will mainly computational analysis and impact of single-cell omics data, and explore the potential of proteomics to infer mode of action.

Module V: For which patients will the drug work and how does it work, really?

In module V, we will consider entry-into human and clinical studies from the perspective of PK/PD modelling, biomarker, and causal inference.


In case you have further questions, comments, and suggestions about the course, please contact the lecturer, Jitao David Zhang, at