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PLS-optimal: a stepwise D-optimal design based on latent variables.

Stefan Brandmaier1, Ullrika Sahlin, Igor V Tetko

  • 1School of Natural Sciences, Linnaeus University, 391 82 Kalmar, Sweden. stefan.brandmaier@gmail.com

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This study introduces a new sequential D-Optimal design using partial least squares (PLS) for efficient compound selection in quantitative structure-activity relationship (QSAR) modeling. This approach significantly improves model performance and reduces experimental costs in drug discovery and risk assessment.

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Area of Science:

  • Computational chemistry
  • Chemometrics
  • Toxicology

Background:

  • Efficient experimental design is crucial for risk assessment (e.g., REACH) and drug discovery, minimizing costs and animal testing.
  • Quantitative structure-activity relationship (QSAR) models predict compound properties, but their development requires careful selection of experimental data.
  • D-Optimal design is a common method for selecting optimal compound subsets for testing, often analyzing data variance.

Purpose of the Study:

  • To develop a novel sequential D-Optimal design approach for selecting compounds for QSAR modeling.
  • To apply this design to latent variables derived from partial least squares (PLS) models, rather than principal components.
  • To evaluate the performance improvement of QSAR models generated using this new method.

Main Methods:

  • A stepwise sequential approach was developed to apply D-Optimal design.
  • The design utilized latent variables derived from partial least squares (PLS) models.
  • The method was tested on four diverse datasets with endpoints relevant to REACH regulations.

Main Results:

  • The sequential D-Optimal selection using PLS latent variables significantly improved QSAR model performance compared to using principal components.
  • Models generated by the PLS-based approach exhibited lower root-mean-square error (RMSE) and higher R-squared (R2) and Q2 values.
  • The performance improvement was statistically significant, particularly when a small number of compounds were selected.

Conclusions:

  • The proposed sequential D-Optimal design based on PLS latent variables offers a more efficient and effective strategy for compound selection in QSAR modeling.
  • This method leads to more accurate predictive models with reduced experimental effort, benefiting applications like drug discovery and chemical risk assessment.
  • The findings highlight the advantage of using PLS-derived latent variables over principal components for optimizing experimental design in chemometrics.