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The One-Class Classification Approach to Data Description and to Models Applicability Domain.

Igor I Baskin1, Natalia Kireeva2, Alexandre Varnek3

  • 1Department of Chemistry, Moscow State University, Moscow 119991, Russia.

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|July 28, 2016
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Summary

This study defines an applicability domain (AD) for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models based on training data density. Models show better predictive performance within high-density areas, improving QSPR model accuracy.

Keywords:
Models applicability domainOne-class classification approachStructure-activity relationshipsStructure-property relationships

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Predictive modeling

Background:

  • Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models are crucial for predicting chemical compound properties.
  • Defining a reliable applicability domain (AD) is essential for ensuring the predictive accuracy and reliability of QSAR/QSPR models.
  • Existing AD definitions can be complex and may not fully leverage the information within the training data.

Purpose of the Study:

  • To develop a novel, data-driven approach for defining the applicability domain (AD) of QSAR/QSPR models.
  • To enhance the predictive performance of QSPR models by accurately identifying reliable prediction regions.
  • To apply the developed AD definition to improve QSPR models for predicting stability constants of metal-ligand complexes.

Main Methods:

  • Associating the applicability domain (AD) with areas of high training data density in the descriptor space.
  • Utilizing a one-class classification (1-SVM) approach to identify high-density regions in a feature space.
  • Generating descriptors and applying kernel-based methods, including chemical graph kernels, for model development.

Main Results:

  • Demonstrated that predictive performance is significantly higher for test compounds located within the high-density training data area.
  • The proposed data-based AD definition is independent of the specific QSAR/QSPR model built on the same training set.
  • The method effectively handles a large number of descriptors and integrates with structured kernel-based approaches.

Conclusions:

  • The developed data-based applicability domain (AD) definition improves the reliability and performance of QSAR/QSPR models.
  • The 1-SVM approach offers a robust and versatile method for AD determination, applicable across various descriptor types and modeling frameworks.
  • The application to QSPR models for metal-ligand complexes shows improved predictive accuracy, highlighting the practical utility of the approach.