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Comparison of optimization algorithms for automated method development of gradient profiles.

Gerben B van Henten1, Jim Boelrijk2, Céline Kattenberg3

  • 1Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, Science Park 904, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), Amsterdam, the Netherlands.

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Summary
This summary is machine-generated.

Differential evolution (DE) excels in dry optimization for liquid chromatography, while Bayesian optimization (BO) is best for search-based methods. Algorithm choice depends on sample complexity and response functions.

Keywords:
Automated method developmentChromatographic response function (CRF)Liquid chromatographyMethod optimizationRetention modeling

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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Computational Chemistry

Background:

  • Optimization algorithms are crucial for gradient elution liquid chromatography (LC) method development.
  • Standardized comparisons of these algorithms across various factors like sample complexity and chromatographic response functions (CRFs) are lacking.
  • Evaluating algorithm performance under different observation modes (in silico vs. search-based) is essential.

Purpose of the Study:

  • To compare the effectiveness of six optimization algorithms (Bayesian optimization, differential evolution, genetic algorithm, CMA-ES, random search, grid search) for gradient elution LC method development.
  • To assess algorithm performance based on data and time efficiency across diverse samples, CRFs, and gradient complexities.
  • To evaluate algorithms in both dry (in silico) and wet (search-based) observation modes.

Main Methods:

  • Six optimization algorithms were compared: Bayesian optimization (BO), differential evolution (DE), genetic algorithm (GA), covariance-matrix adaptation evolution strategy (CMA-ES), random search, and grid search.
  • A multi-linear retention modeling framework was used for assessment.
  • Algorithms were evaluated across diverse samples, CRFs, and gradient segments in dry (in silico) and wet (search-based) observation modes.

Main Results:

  • Differential evolution (DE) demonstrated high data and time efficiency for dry optimization.
  • Bayesian optimization (BO) excelled in data efficiency and was most effective for search-based optimization (<200 iterations) but impractical for dry optimization due to computational scaling.
  • Chromatographic response functions (CRFs) and sample complexity significantly influenced algorithm efficiency, highlighting the need for better benchmark samples and understanding CRF-induced complexity.

Conclusions:

  • DE is a competitive choice for dry optimization in gradient LC method development.
  • BO is highly effective for data-efficient, search-based optimization but less suitable for large-scale dry optimization.
  • The choice of optimization algorithm should consider sample characteristics and CRFs for efficient method development.