天美传媒

Abstract

Chiral chromatography is a crucial technique in separating enantiomers, pivotal for applications in pharmaceuticals, biotechnology, and environmental analysis. However, traditional methods often face challenges in precision, efficiency, and scalability. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into chiral chromatography presents a transformative approach to overcoming these limitations. AI and ML algorithms can optimize chromatographic conditions, enhance the design of chiral selectors, and improve real-time data analysis, leading to increased precision and operational efficiency. By leveraging data-driven insights, these technologies enable more accurate predictions of separation outcomes and streamline method development. This abstract reviews the current advancements in AI and ML applications within chiral chromatography, discussing their impact on optimizing chromatographic processes, accelerating method development, and achieving higher resolution and reproducibility. The incorporation of AI and ML not only addresses existing challenges but also opens new avenues for innovation in chiral separation techniques.