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Chiral Chromatography and Artificial Intelligence Integration in Enantiomers Separation
Imran Ali1, Mouslim Messali2, Ann Gogolashvili3
1Department of Chemistry, Jamia Millia Islamia, New Delhi, India.
Artificial intelligence and machine learning enhance chiral chromatography, a sensitive separation science technique. This integration addresses challenges in separation, detection, and reproducibility for research and industry.
Area of Science:
- Separation Science
- Analytical Chemistry
- Computational Chemistry
Background:
- Chiral chromatography is crucial for separating enantiomers due to their similar properties.
- Current limitations include challenges in efficient separation, detection, and reproducibility.
- Expertise is required for effective chiral separations.
Purpose of the Study:
- To review the integration of artificial intelligence (AI) and machine learning (ML) with chiral chromatography.
- To discuss current developments, future requirements, and applications of this integration.
- To provide insights for research and industrial applications.
Main Methods:
- Analysis of software and models for AI/ML integration in chiral chromatography.
- Method development and optimization strategies for integrated systems.
- Review of real-world applications of AI/ML-enhanced chiral chromatography.
Main Results:
- AI and ML offer solutions to improve separation efficiency, detection sensitivity, and reproducibility in chiral chromatography.
- Various software and models are available for integration, aiding method development.
- Successful applications in real-life samples demonstrate the potential of this synergistic approach.
Conclusions:
- Integrating AI/ML with chiral chromatography significantly overcomes existing limitations.
- This synergy enhances analytical capabilities for chiral compound analysis.
- The review highlights challenges, recommendations, and future perspectives for broader adoption.
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