ArticleJournal of chemical education2025
Illuminating the Interface of Blocc Chemistry and Data Science: An Introduction to K‑Nearest Neighbor Analysis and K‑Medoids Clustering.
Article in Journal of chemical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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12 authors.
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Abstract
The discovery of new chemical functions is being accelerated by blocc chemistry for iterative carbon-carbon bond formation coupled to artificial intelligence (AI) and machine learning (ML) techniques; yet, these technologies are largely absent from introductory undergraduate courses. This lab emphasizes the utility of data science tools through the context of discovering chemical function. We propose that teaching blocc chemistry provides student scientists the ability to explore functional data from frontier research generated for the purpose of this labeven with minimal knowledge of organic chemistry. Here, we report the first laboratory experiment in a sequence designed to illuminate the AI-Chemistry interface. This first experiment teaches students the fundamental principles of K-Nearest Neighbor (KNN) analysis and K-Medoids clustering to predict unknown chemical functions through the lens of blocc chemistry.
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