Smart Enantioseparation: A Critical Review of Instrumentation, Automation, and Data-Driven Strategies in Analytical Chirality

Enantioseparation remains essential in pharmaceutical and bioanalytical sciences, yet method development continues to rely heavily on empirical column screening and experience-driven optimization. Although recent advances in miniaturized platforms, hyphenated detection systems, and machine-learning-assisted modeling have expanded the analytical toolkit available for chiral analysis, these developments are often reported in isolation, without a unifying systems-level framework. This critical review synthesizes advances in chromatographic and electrophoretic enantioseparation, spanning liquid chromatography, capillary electrophoresis, supercritical fluid chromatography, micro- and nano-scale platforms, and mass spectrometry-coupled systems. Particular emphasis is placed on the integration of mechanistic descriptors, automation strategies, closed-loop optimization, and data-driven modeling within modern chiral method development. Rather than cataloguing technologies, the review evaluates how these components collectively enhance selectivity prediction, parameter optimization, reproducibility, and sustainability. Key challenges, including data quality constraints, model interpretability, regulatory alignment, and scalability, are critically examined to distinguish mature implementations from exploratory strategies. By integrating instrumentation, control logic, and computational intelligence within a coherent analytical framework, this review provides structured guidance for transitioning from empirically driven chiral separations toward more transparent, adaptive, and validation-ready analytical systems.