Pharma students today know what it feels like to work with real data on real problems. From their first semester they have been building, failing, iterating and learning. They leave university with portfolios that show what they can actually do, a deep understanding of how data shapes every part of the medicines lifecycle, and the confidence to contribute wherever their pharma career takes them. At the end of every dataset, every model and every curriculum decision, there is a patient. That is what makes the work matter. Data science is part of the toolkit, not a subject on the side. Domain knowledge has not been crowded out. It has been made more powerful by the ability to work with data. Graduates are fluent in both.

Teaching has changed too. Teachers across UCPH and SDU act more as supervisors than lecturers, following the energy and building from genuine enthusiasm rather than mandating change from above. They work in networks where pharma expertise and data science expertise meet and understand each other. Courses are designed with flexibility to meet students where they are, balancing problem based learning with the fundamentals. How you teach data science in a pharma context has become a discipline in its own right. What holds all of this together is an intentional cultural shift. CPDSE has become the natural meeting point for teachers, students, researchers and the pharma industry, a community built around data science and pharma together, strong enough to prevent fragmentation even at the level of individual course teams. Belonging is designed in, not left to chance. No one learns alone because we built it that way. The questions about AI are not just open. They are urgent. What we are building is the domain grounded judgment that makes it possible to work with AI well: to know what a model is actually doing, to recognise where it fails, and to take responsibility for the decisions it informs.

“Medicines are made by people who understand data. We build a community flexible enough to keep up with the world and connected enough that no one gets left behind.”

With ambition, CPDSE

We (Albert, Stefan, Sarah, Sebastian, Tommy, Morten and Lykke) wrote a postcard to ourselves, ten years from now. The shared postcard is what you can read above.

The awarded projects are:

• AI to strengthen transparency and reproducibility in real-world evidence (RWE) generation
• LLM-assisted patient–pharmacist conversation to improve communication education
• A Neurosymbolic Framework for Literature-Driven GPCR Interaction Modeling
• From mechanism to prediction: forecasting ligand-specific outcomes across GPCR variants with MD-derived biophysical descriptors
• Modeling release and redistribution of hydrophobic drugs from lipid nanocarriers

These five projects represent a strong and diverse research foundation. We expect the PhD students involved to play an important role in the collaborative research activities emerging from CPDSE, including student-driven science exchange initiatives where participants can shape the format and direction of the work with center support.

Congratulations again to all awardees — we look forward to the next steps!

Our guiding theme is “Systematize, Service, Expose.” More than 25 contributors across the University of Copenhagen and the University of Southern Denmark have already committed to concrete deliverables.

Systematize: Building the Foundation for Scalability We are investing in the governance and technical infrastructure needed to make data science a durable part of pharmaceutical education. This includes standardized, one-click coding environments in R and Python, a shared code culture via GitHub, and a refreshed website, ensuring our work is reusable, scalable, and consistently communicated.

Service: Supporting Faculty and Students CPDSE exists to strengthen education in our partner departments. We are collaborating with course teams to integrate data science into existing curricula, launching drop-in support sessions and student code clubs, and offering workshops and bootcamps to equip staff with relevant data science skills.

Expose: Bridging Academia and Industry We are connecting our students and research with the broader pharmaceutical world through industry-linked projects, data science exchange events, and a methodological consulting framework. We are also growing our network of affiliated faculty and increasing our presence in external forums.

As we build more robust systems, we remain committed to a founding principle: safe enough to try. The pursuit of the perfect infrastructure should never delay the services our community needs today.