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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Reversed-Phase Liquid Chromatography (RPLC) is vital for pharmaceutical compound analysis.
  • Optimizing RPLC conditions is experimentally intensive and time-consuming.
  • Quantitative Structure Retention Relationship (QSRR) models offer a computational alternative to predict retention times.

Purpose of the Study:

  • To develop and compare multiple QSRR models for predicting retention times in RPLC.
  • To evaluate model performance across different pH conditions (2.7, 3.5, 6.5, 8.0).
  • To establish a reliable prediction framework for small molecule separation.

Main Methods:

  • Development of QSRR models using linear and non-linear regression algorithms (MLR, SVR, LASSO, RF, GBR).
  • Model building and validation across five distinct pH values.
  • Ensemble modeling using stacking and application domain filtering (k-NN) for prediction reliability.

Main Results:

  • Comparison of the predictive accuracy of various QSRR algorithms.
  • Identification of optimal QSRR models for specific pH conditions.
  • Demonstration of stacking ensemble effectiveness and the utility of the k-NN domain filter.

Conclusions:

  • QSRR modeling provides an efficient computational approach to predict RPLC separations.
  • The developed models and filters aid analytical chemists in optimizing chromatographic conditions.
  • This study facilitates faster compound prioritization and method development in pharmaceutical analysis.