AI- & data-driven drug design – An introduction
Graduate School of Health and Medical Sciences at University of Copenhagen
This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH.
Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list.
The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee.
Learning objectives
A student who has met the objectives of the course will:
1. Know in which online databases and tools to find drug, target and disease information and be able to make informed decisions influencing drug discovery projects.
2. Know the principles and tools for structure-based drug design and be able to formulate testable hypotheses for the optimization of small molecule drug leads.
3. Know of state-of-the-art methods for protein modeling and peptide design (e.g. AlphaFold and RoseTTA) as well as be able to use these to solve target- and drug-related drug design problems.
4. Extend his/her skills in analysis and visualization of complex datasets using MS Excel.
5. Know of ML methods for small molecule and peptide design and be able to ML to identify the most important features in a complex drug/target-related dataset.
You will:
• Get a taste of computational drug design areas and methods to find out what you like best
• Acquire a toolbox enabling you to solve problems related to drugs and their targets
• Better understand and communicate with computational colleagues in an interdisciplinary team
• Receive information about more follow-up courses to advance your skills in areas of interest
Content
One-week course in which each of the five days will be dedicated to: 1) Online databases and tools, 2) Structure-based drug design (small molecules), 3) Protein modeling and peptide design (AlphaFold, RoseTTA etc.), 4) Data analysis and visualization (non-ML) and 5) Data analysis and visualization using ML. Each day, there will be three lectures (9-12), lunch (12-13) and a hands-on workshop (13-17).
Participants
The course is primarily offered to PhD students who have completed undergraduate courses in pharmacy, medicine, pharmacology, biochemistry, biology, chemistry, molecular biology or similar topics. In addition, the course is offered to researchers within the pharmaceutical industry or biotech. Students enrolled in part-time master's programs at the University of Copenhagen may also participate in the course.
Relevance to graduate programmes
The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:
- Pharmaceutical Sciences (Drug Research Academy)
- Molecular Mechanisms of Disease
- Biostatistics and Bioinformatics
Language
English
Form
The course is organized as a one-week course and comprises about 13 scientific lectures, 4 hands-on workshops (3-4 hours), discussions and short student presentations. The participants will be supplied with study material that can be downloaded from the course homepage.
Course director
David Gloriam, Professor, David.gloriam@sund.ku.dk
Teachers
• Industrial experts with experience of drug discovery
• International researchers with leading profiles within the given topics
• University of Copenhagen researchers (from PhD to Professors) sharing knowledge and methods used in their projects
• …See the detailed course description for tentative teacher names, titles and affiliations.
Dates
9 – 13 November 2026
Course location
Universitetsparken 2, DK-2100 Copenhagen Ø
Registration
Please register before 1 October 2026
Expected frequency
Yearly, in November
Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment.
Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.