On some aspects of validation of predictive quantitative structure–activity relationship models

  title={On some aspects of validation of predictive quantitative structure–activity relationship models},
  author={Kunal Roy},
  journal={Expert Opinion on Drug Discovery},
  pages={1567 - 1577}
  • K. Roy
  • Published 26 November 2007
  • Biology, Chemistry
  • Expert Opinion on Drug Discovery
The success of any quantitative structure–activity relationship model depends on the accuracy of the input data, selection of appropriate descriptors and statistical tools and, most importantly, the validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose. This review focuses on the importance of validation of quantitative structure–activity relationship models and different methods of validation… 

Prediction reliability of QSAR models: an overview of various validation tools

The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness and a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues.

On further application of r  m2 as a metric for validation of QSAR models

Validation is a crucial aspect for quantitative structure–activity relationship (QSAR) model development. External validation is considered, in general, as the most conclusive proof of predictive

Comparative Studies on Some Metrics for External Validation of QSPR Models

This report questions the appropriateness of the common practice of the "classic" approach of external validation based on a single test set and derives a conclusion about predictive quality of a model on the basis of a particular validation metric.

History of Quantitative Structure–Activity Relationships

The development of databases that encapsulate significant data on variables and models that allow for rapid retrieval, usage, and validation of chemical reactions and biological interactions is included in this analysis of QSAR.

On Two Novel Parameters for Validation of Predictive QSAR Models

A test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

Is regression through origin useful in external validation of QSAR models?

  • A. ShayanfarShadi Shayanfar
  • Biology
    European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
  • 2014



The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

A set of simple guidelines for developing validated and predictive QSPR models is presented, highlighting the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and some algorithms that can be used for this purpose.

Assessment of Prediction Confidence and Domain Extrapolation of Two Structure–Activity Relationship Models for Predicting Estrogen Receptor Binding Activity

Two QSAR models based on different data sets for classification of chemicals according to their ability to bind to the estrogen receptor are reported on, both of which had poor accuracy for chemicals within the domain of low confidence, whereas good accuracy was obtained for those within thedomain of high confidence.

Guidelines for developing and using quantitative structure‐activity relationships

The proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects.

Assessing the reliability of a QSAR model's predictions.

  • L. HeP. Jurs
  • Chemistry, Biology
    Journal of molecular graphics & modelling
  • 2005

Validation tools for variable subset regression

A validation protocol is presented briefly and two of the tools which are part of this protocol are introduced in more detail, which can be used to determine the complexity and with it the stability of models generated by variable selection.

On Selection of Training and Test Sets for the Development of Predictive QSAR models

It is proposed that K-means-cluster-based division of training and prediction sets can be used as a reliable method of division of data set into training and test sets for developing predictive QSAR models.

Principles of QSAR models validation: internal and external

Evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes.

Beware of q2!

QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review

Methods and criteria for estimating AD through training set interpolation in descriptor space and response space are reviewed and it is proposed that response space should be included in the training set representation.