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Cross-column density functional theory-based quantitative structure-retention relationship model development powered

Sargol Mazraedoost1, Petar Žuvela1, Szymon Ulenberg2

  • 1Intelligent Systems Laboratory, Department of Chemical Engineering, Pukyong National University, Busan, 48513, Republic of Korea.

Analytical and Bioanalytical Chemistry
|March 20, 2024
PubMed
Summary
This summary is machine-generated.

This study developed cross-column quantitative structure-retention relationship (QSRR) models using machine learning and quantum mechanical descriptors to predict retention times in high-performance liquid chromatography (HPLC). Gradient boosting models achieved excellent predictive performance across different columns and conditions.

Keywords:
CheminformaticsDensity functional theory (DFT)Machine learning (ML)Quantitative structure-retention relationship (QSRR)Retention time predictionReversed-phase high-performance liquid chromatography (RP-HPLC)

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Chromatography

Background:

  • Quantitative structure-retention relationship (QSRR) modeling predicts analyte retention times using molecular descriptors.
  • Existing QSRR models are often column-specific, limiting their application across different high-performance liquid chromatography (HPLC) systems.
  • Developing universal QSRR models applicable across various columns and conditions is a significant challenge.

Purpose of the Study:

  • To develop and evaluate machine learning (ML)-based QSRR models capable of predicting retention times across multiple reversed-phase HPLC columns.
  • To investigate the utility of quantum mechanical (QM) descriptors in conjunction with experimental parameters for cross-column QSRR modeling.
  • To compare the performance of different ML algorithms, including PLS, RR, RF, and GB, for predicting chromatographic retention times.

Main Methods:

  • Calculated quantum mechanical (QM) descriptors using density functional theory (DFT) for 15 aromatic analytes.
  • Developed QSRR models using four ML algorithms: partial least squares (PLS), ridge regression (RR), random forest (RF), and gradient boosting (GB).
  • Incorporated column characteristics (particle size, pore size) and experimental conditions (temperature, gradient time) as additional descriptors.

Main Results:

  • The gradient boosting (GB) QSRR model achieved the highest predictive performance, with a Q² of 0.989 and RMSEP of 0.749 min on the test set.
  • Key influential descriptors included solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η), highlighting the role of electrostatic interactions and hydrophobicity.
  • Ensemble methods (GB, RF) demonstrated superior ability in capturing local variations in retention times across diverse experimental setups.

Conclusions:

  • Machine learning algorithms, particularly ensemble methods like gradient boosting, are highly effective for developing cross-column QSRR models.
  • Quantum mechanical descriptors significantly contribute to predicting retention times, emphasizing the importance of molecular electronic properties and hydrophobicity.
  • This study demonstrates the potential of universal QSRR models to optimize chromatographic analysis and reduce the need for column-specific method development.