Machine learning methods are increasingly leveraged in disparate domains of research. Herein, we describe our curriculum design to introduce undergraduate students to research applications through a series of course assignments and a competition among peers to inspire other educators. For the selected course-long problem of predicting which drugs might interact with specific proteins, we leveraged state-of-the-art tools for representing drug and protein sequences and challenged students to develop unique solutions competitive with a current state-of-the-art model with the expectation that top-ranking solutions would be used to predict novel drugable targets within the SARS-CoV-2 proteome to possibly treat COVID19 patients. We motivate this curriculum design based on related competition frameworks that have led to notable research advancements and contributed to machine learning pedagogy. We describe the overall course structure and detail how the assignments were tailored to a selected open research question while developing student understanding of machine learning. We outline the lessons learned from this new undergraduate curriculum design and describe how it may be adapted to similar courses. From our experience, the top student solutions were ultimately combined using a stacked classifier to create a publishable solution representing an actual research contribution. We highly recommended introducing undergraduate students to open research applications early in their program to encourage them to consider pursuing a career in research.