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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Study of Data Set Modelability: Modelability, Rivality, and Weighted Modelability Indexes.

Irene Luque Ruiz1, Miguel Ángel Gómez-Nieto1

  • 1Department of Computing and Numerical Analysis , University of Córdoba , Campus de Rabanales , Albert Einstein Building, E-14071 Córdoba , Spain.

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This study introduces a refined modelability index (MODI) for quantitative structure-activity relationship (QSAR) models. The enhanced index accurately predicts model performance, saving time in data set selection and refinement.

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Area of Science:

  • * Cheminformatics
  • * Computational Chemistry
  • * Machine Learning in Drug Discovery

Background:

  • * Quantitative Structure-Activity Relationship (QSAR) models are crucial for predicting molecular activity.
  • * Assessing data set modelability early saves time and resources in QSAR model development.
  • * The existing modelability index (MODI) provides a standardized measure but can be enhanced.

Purpose of the Study:

  • * To propose a more formal and extended formulation of the modelability index (MODI).
  • * To introduce the rivality index for identifying classifiable molecules and activity cliffs.
  • * To develop a weighted modelability index correlated with QSAR model classification accuracy.

Main Methods:

  • * Formalizing the calculation of the modelability index to include nearest neighbors within each data class.
  • * Calculating the rivality index based on the distribution of nearest neighbors across classes.
  • * Weighting the rivality index by neighborhood cardinality to create a weighted modelability index.

Main Results:

  • * The new formulation enables the calculation of the rivality index, indicating potential classification issues.
  • * The weighted modelability index demonstrates a high correlation (r² > 0.9) with the actual QSAR correct classification rate (QSAR_CCR).
  • * The weighted modelability index shows near-ideal performance metrics: slopes close to 1 and bias close to zero across various classification algorithms.

Conclusions:

  • * The proposed weighted modelability index is a robust predictor of QSAR model performance.
  • * This enhanced index can significantly improve the efficiency of data set selection and refinement in QSAR studies.
  • * The methodology offers a valuable tool for cheminformatics and computational chemistry practitioners aiming for reliable predictive models.