Keeping pace in the age of innovation: The perspective of Dutch pharmaceutical science students on the position of machine learning training in an undergraduate curriculum

Abstract

Background: Over the years, approaches of the pharmaceutical industry to discover and develop drugs have changed rapidly due to new scientific trends. Among others, they have started to explore Machine Learning (ML), a subset of Artificial Intelligence (AI), as a promising tool to generate new hypotheses regarding drug candidate selections for clinical trials and to predict adverse side effects. Despite these recent developments, the possibilities of ML in pharmaceutical sciences have so far hardly penetrated the training of pharmaceutical science students. 1, 2 Therefore, as part of an elective course, an introductory module on ML was developed at Utrecht University, Department of Pharmaceutical Sciences. Objective: The aim of this study was to assess student’ views on the module set-up, and their perspectives on ML within pharmaceutical science curricula. Methods: Semi-structured interviews over three years were conducted with 15 students participating in the module. Results: The students valued the well-designed and effective delivered module. They were personally motivated to learn more about ML in a future master or research internship. The students now perceive a lack of possibilities for ML training in pharmaceutical sciences education and indicate the value of incorporating ML opportunities for their future career. Conclusion: Integrating ML training into pharmaceutical sciences curricula is needed to keep future drug researchers up to date with drug research advancements, enhancing their skills, academic development, and career prospects.