Objective:

To explore the potential of AI in improving the prediction of retention times in liquid chromatography, thereby enhancing efficiency in the pharmaceutical industry and accelerating drug development.

Key Findings:

• AI can process vast amounts of data quickly, improving tasks in analytical chemistry.
• Current methods for predicting retention times in LC are inadequate due to their reliance on idealized models or limited data sets.
• AI-based methods can identify complex patterns in data that traditional models cannot, such as interactions between analytes and stationary phases.

Interpretation:

AI has the potential to revolutionize liquid chromatography by providing more accurate predictions of retention times, which is crucial for drug development and quality control in pharmaceuticals, ultimately leading to faster delivery of safe and effective treatments.

Limitations:

• AI models are susceptible to errors in input data, leading to inaccurate predictions.
• ML approaches cannot fully capture the fundamental microscopic processes that determine retention times, but ongoing research aims to integrate these insights.

Conclusion:

While AI presents a promising avenue for improving retention time predictions in liquid chromatography, challenges remain in data quality and the fundamental understanding of retention mechanisms, necessitating further research and development.