Imran Ali, Mouslim Messali, Ann Gogolashvili, Khaled Sekkoum
Chiral chromatography is the most sensitive technique in separation science due to the similar properties of the enantiomers, which require highly expert hands. This sort of chromatography has various limitations and issues, especially in efficient separation, detection, and reproducibility. These issues can be tackled by integrating chiral chromatography with artificial intelligence and machine learning approaches. This review article describes the present development in chiral chromatography integration with artificial intelligence and machine learning and future requirements. The most important aspects discussed in this article are the analysis of various software and models needed for integration, method development and optimization of chiral chromatography, and applications of artificial intelligence and machine learning integrated chiral chromatography in real-life samples. Besides, the challenges, recommendations, and future perspectives of artificial intelligence and machine learning integrated chiral chromatography are discussed. This article will be highly useful for applying artificial intelligence and machine learning integration in chiral chromatography in research and industrial applications.