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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Structure-Activity Relationships and Drug Design01:28

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Related Experiment Video

Updated: Jun 22, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Published on: May 9, 2025

On two novel parameters for validation of predictive QSAR models.

Partha Pratim Roy1, Somnath Paul, Indrani Mitra

  • 1Department of Pharmaceutical Technology, Division of Medicinal and Pharmaceutical Chemistry, Drug Theoretics and Cheminformatics Lab, Jadavpur University, Kolkata, India. partha_chemju@yahoo.co.in

Molecules (Basel, Switzerland)
|May 28, 2009
PubMed
Summary

New validation parameters, r(m)(2) and R(p)(2), offer a stricter assessment for quantitative structure-activity relationship (QSAR) models. These novel metrics supplement traditional validation, ensuring more reliable predictive models, especially for regulatory decisions.

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Area of Science:

  • * Computational Chemistry
  • * Medicinal Chemistry
  • * Cheminformatics

Background:

  • * Quantitative Structure-Activity Relationship (QSAR) modeling is vital for drug discovery and development.
  • * Traditional validation parameters like Q(2) (internal) and R(2) (external) have limitations in rigorously assessing model reliability.
  • * A need exists for more stringent validation metrics to ensure the predictive power and robustness of QSAR models.

Purpose of the Study:

  • * To introduce and evaluate two novel validation parameters, r(m)(2) and R(p)(2), for QSAR modeling.
  • * To demonstrate the enhanced stringency of these new parameters compared to traditional validation metrics.
  • * To assess the utility of r(m)(2) and R(p)(2) in selecting the best QSAR models and ensuring regulatory compliance.

Main Methods:

  • * Development of multiple QSAR models using three diverse datasets of moderate to large size.
  • * Application of traditional validation parameters: leave-one-out Q(2) and predictive R(2).
  • * Calculation and analysis of novel validation parameters: r(m)(2) (overall, LOO, test) and R(p)(2).

Main Results:

  • * Many QSAR models met conventional validation criteria (Q(2), R(2)(pred)) but failed the novel r(m)(2) and R(p)(2) tests.
  • * The novel parameters r(m)(2) and R(p)(2) provided a stricter validation, identifying less reliable models.
  • * r(m)(2) and R(p)(2) proved effective in discriminating between comparable QSAR models, aiding in the selection of superior ones.

Conclusions:

  • * The novel parameters r(m)(2) and R(p)(2) offer a more rigorous validation for predictive QSAR models.
  • * Incorporating r(m)(2) and R(p)(2) is recommended for a stricter assessment of QSAR model acceptability.
  • * These parameters are particularly valuable when QSAR models are intended for regulatory decision-making.