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Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
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Do you know your r2?

Alex Avdeef1

  • 1in-ADME Research, 1732 First Avenue #102, New York, NY 10128 USA.

ADMET & DMPK
|March 18, 2022
PubMed
Summary

Understanding drug solubility prediction requires careful selection of statistical metrics. Different definitions of R-squared and RMSE can obscure systematic errors in models, particularly for novel chemical spaces.

Area of Science:

  • Computational Chemistry
  • Drug Discovery Informatics
  • Medicinal Chemistry

Background:

  • Drug solubility prediction is crucial in drug discovery, often relying on various computational programs.
  • Standard statistical metrics like R-squared (coefficient of determination) and Root-Mean-Square Error (RMSE) are used to evaluate prediction model performance.
  • Inconsistencies in the definitions of these statistical indices across different software can lead to misinterpretation.

Purpose of the Study:

  • To review the definitions of three common statistical metrics: R-squared (model validation, bias compensation, Pearson) and RMSE.
  • To highlight how variations in these statistical indices can differently indicate systematic errors in solubility prediction models.
  • To clarify the ambiguity in statistical reporting for drug solubility prediction, especially for compounds beyond the 'Rule of 5' chemical space.
Keywords:
coefficient of determinationlinear correction coefficientlinear regressionroot-mean-square error

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Main Methods:

  • Review of definitions for R-squared and RMSE statistics.
  • Analysis of how different statistical index definitions impact the indication of systematic errors.
  • Examination of prediction model scatter plots, including 'bias-tilt' systematic errors.

Main Results:

  • Different definitions of R-squared and RMSE can lead to varied interpretations of model accuracy.
  • Systematic errors in solubility prediction models, particularly 'bias-tilt' scatter, may be masked or exaggerated by the choice of statistical index.
  • The statistical indices used in recent publications for predicting solubility of 'beyond the Rule of 5' molecules were found to be unclear.

Conclusions:

  • The choice of statistical indices significantly impacts the assessment of drug solubility prediction models.
  • Clear and consistent reporting of statistical definitions is essential for accurate model evaluation and comparison.
  • Addressing the 'bias-tilt' systematic error requires careful consideration of the statistical methods employed, especially for novel chemical entities.