Prior Knowledge‐Based Bayesian Optimization With Real‐Time Process Analytical Technology for the Transition From Batch to Continuous Flow Biocatalysis: Lipase‐Mediated Dynamic Kinetic Resolution

The synthesis of high‐value chiral pharmaceutical intermediates typically requires efficient, scalable, and stereoselective processes. Here, we report an integrated batch‐flow chemoenzymatic platform for the asymmetric synthesis of ( S )‐(‐)‐2‐acetoxy‐2‐phenylethanenitrile with Novozym 435. This system uniquely combines a continuous‐flow packed‐bed reactor with inline liquid–liquid separation and real‐time monitoring via inline FTIR spectroscopy. To accelerate process intensification, a meta‐learning‐based Bayesian optimization algorithm was employed to systematically identify optimal reaction conditions. This data‐driven approach enables the reaction to be scaled up to the gram scale at a substrate concentration of 0.2 M. The optimized system achieved an enantiomeric excess (ee) of 95% and a conversion of 57%, notably exceeding the theoretical 50% limit of kinetic resolution. Mechanistic investigations, supported by partial least squares modeling of spectral data, revealed that this enhanced conversion arises from spontaneous racemization of the unreacted ( R )‐enantiomer, confirming a dynamic kinetic resolution pathway. This work establishes a paradigm for the green and scalable production of chiral cyanohydrins via integrated flow chemistry and real‐time spectroscopic monitoring, with broader applicability to diverse chiral building blocks.