Machine Learning for Efficient Substitution Control toward Azido-Substituted <scp>l</scp> -Sugar Synthesis via Flow Chemistry

Over functionalization of sugars under condition-dependent constraints without disrupting their native architecture remains a significant challenge in vaccine development. Here, we report an AI-guided, automated flow platform with variable reaction conditions that enables azide incorporation at the C2 and C2–C4 positions of l-rhamnose and l-fucose derivatives, achieving yields of up to 90–97%. This approach delivers a safe handling of NaN3, minimum human intervention, and approximately 3000-fold enhancement in space–time yield compared to conventional batch synthesis. Subsequent in-flow Cu-catalyzed azide–alkyne cycloaddition (CuAAC) affords mono- and ditriazoles, offering a scalable route to glycoconjugates for both medicinal and material applications.