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Updated: Jan 19, 2026

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Localised quantitative structure-retention relationship modelling for rapid method development in reversed-phase high

Soo Hyun Park1, Mauro De Pra1, Paul R Haddad2

  • 1Thermo Fisher Scientific, Germering, Bavaria, Germany.

Journal of Chromatography. A
|September 19, 2019
PubMed
Summary

This study introduces a novel dual clustering quantitative structure-retention relationship (QSRR) approach for predicting compound retention times in reversed-phase chromatography. This method enhances in silico column scoping for efficient method development, reducing experimental effort.

Keywords:
Column scopingP-ratio clusteringQSRRReversed-phase liquid chromatographySecond dominant interaction (SDI)-based clustering

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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Computational Chemistry

Background:

  • Quantitative structure-retention relationships (QSRR) can predict retention times on reversed-phase (RP) columns using solute hydrophobicity and column parameters.
  • Accurate prediction models are crucial for in silico column scoping in RP method development to streamline the selection of optimal chromatographic conditions.

Purpose of the Study:

  • To develop a new dual clustering-based localized QSRR approach for predicting solute hydrophobicity coefficient (η").
  • To enable accurate in silico column scoping for reversed-phase liquid chromatography (RPLC) method development.

Main Methods:

  • A dual clustering approach combining P-ratio and second dominant interaction (SDI)-based clustering was employed.
  • Quantitative structure-retention relationship (QSRR) models for η' values were derived using a genetic algorithm-partial least square regression (GA-PLS).
  • Predicted η' values and stationary phase hydrophobicity parameters (H) were used in the hydrophobic subtraction model (HSM) to predict retention times.

Main Results:

  • The QSRR models for η' achieved a predictive squared correlation coefficient (Qext(F2)2) of 0.83.
  • Predicted retention times using the developed models showed excellent accuracy (Qext(F2)2 = 0.90) and predictability.
  • Experimental verification on six Thermo Scientific columns demonstrated good agreement between predicted and observed retention times (Qext(F2)2 up to 0.95).

Conclusions:

  • The proposed dual clustering-based QSRR approach provides accurate predictions for solute hydrophobicity and retention times.
  • This method facilitates effective in silico column scoping, reducing the need for extensive experimental screening of reversed-phase stationary phases.
  • The approach is validated experimentally, showing its practical utility in chromatographic method development.