Retention Time Prediction in High-Performance Liquid Chromatography Using Random Forest Regression
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High-Performance Liquid Chromatography is widely used for compound analysis, where retention time (RT) serves as a critical parameter. This study employs a Random Forest Regression model to predict RT based on molecular descriptors such as molecular weight, partial charge, partition coefficient, and topological polar surface area. The model successfully predicted the retention time with high similarity to the real data, thereby validating its accuracy. This study highlights the potential of machine learning in optimizing chromatographic analysis.
