โก Quick Summary
A recent study explored the integration of Machine Learning (ML) training in the undergraduate curriculum for pharmaceutical science students at Utrecht University. The findings revealed that students are eager for more ML opportunities, recognizing its potential to enhance their future careers in drug research.
๐ Key Details
- ๐ Participants: 15 students from Utrecht University
- ๐งฉ Focus: Introductory module on Machine Learning in pharmaceutical sciences
- โ๏ธ Methodology: Semi-structured interviews conducted over three years
- ๐ Outcome: Positive feedback on module design and delivery
๐ Key Takeaways
- ๐ ML is increasingly recognized as a vital tool in drug discovery and development.
- ๐ก Students expressed a strong desire for more ML training in their education.
- ๐ฉโ๐ฌ The module was well-received, indicating effective teaching methods.
- ๐ Students feel unprepared for the ML demands in their future careers.
- ๐ Incorporating ML training could significantly enhance academic and career prospects.
- ๐ Continuous innovation in pharmaceutical sciences necessitates updated curricula.
๐ Background
The pharmaceutical industry is undergoing rapid transformation, driven by advancements in technology and science. Among these innovations, Machine Learning (ML) has emerged as a promising approach to streamline drug discovery processes, from hypothesis generation to predicting adverse effects. However, the integration of ML into educational programs for pharmaceutical sciences has lagged behind, prompting the need for curriculum updates.
๐๏ธ Study
This study was conducted at Utrecht University, where an elective module on Machine Learning was introduced within the Department of Pharmaceutical Sciences. Over three years, researchers conducted semi-structured interviews with 15 students who participated in the module, aiming to gather insights on their experiences and perspectives regarding ML training in their curriculum.
๐ Results
The results indicated that students found the module to be well-designed and effectively delivered. They expressed a strong motivation to pursue further learning in ML, particularly in masterโs programs or research internships. Notably, students highlighted a significant gap in ML training within their current educational framework, emphasizing its importance for their future careers in pharmaceutical sciences.
๐ Impact and Implications
The implications of this study are profound. By integrating Machine Learning training into pharmaceutical science curricula, educational institutions can better prepare students for the evolving landscape of drug research. This integration not only enhances students’ skills but also aligns their education with industry demands, ultimately improving their career prospects and contributions to the field.
๐ฎ Conclusion
The findings from this study underscore the critical need for educational reforms in pharmaceutical sciences to include Machine Learning training. As the industry continues to innovate, it is essential that future drug researchers are equipped with the necessary skills to navigate these advancements. Embracing ML in education will not only enhance academic development but also empower students to thrive in their future careers.
๐ฌ Your comments
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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.
Author: [‘Kidwai S’, ‘Rojas-Velazquez D’, ‘Lopez-Rincon A’, ‘Kraneveld AD’, ‘Oberski DL’, ‘Meijerman I’]
Journal: Curr Pharm Teach Learn
Citation: Kidwai S, et al. 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. 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. 2024; 17:102231. doi: 10.1016/j.cptl.2024.102231