Sivakumar Thanikachalam, Valliappan Kannappan
Chirality remains a central yet inconsistently operationalized concept in pharmaceutical analysis. Although regulatory frameworks emphasize stereochemical characterization and enantiomeric control, practical analytical decisions are often guided by expert intuition and historical precedent. These include determining when chiral separation is necessary, when achiral methods are sufficient, and how to balance analytical rigor with analytical complexity. With increasing molecular complexity, accelerated development timelines, and heightened regulatory expectations, such intuition-driven approaches face growing limitations. Artificial intelligence (AI) has emerged as a powerful enabler across pharmaceutical sciences; however, its role in chiral decision-making has not been systematically articulated. This perspective reframes chirality as a decision-driven discipline and proposes a concept-driven, framework-oriented approach in which AI functions as a cognitive decision-support layer rather than a replacement for stereochemical expertise. It integrates stereochemistry, decision science, explainable artificial intelligence (XAI), and regulatory reasoning into a coherent approach for chiral decision-making in pharmaceutical analysis. Unlike existing AI applications that primarily focus on analytical optimization, this perspective proposes a decision-oriented framework for evaluating when chiral analysis is warranted and how analytical decisions may be supported using multidisciplinary evidence. The proposed framework bridges traditional stereochemical reasoning with data-driven intelligence and provides a structured basis for future chiral decision-support systems. As a framework-oriented perspective, this work is intended to stimulate discussion and guide future empirical evaluation, including prototype decision-support systems, curated datasets, and prospective validation studies, rather than present a validated AI implementation.