A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to
effectively treat experimental error in the training and evaluation of computational models. It …

One of the important applications of quantitative structure-activity relationship (QSAR)
models is to fill data gaps by predicting a given response property or activity from known …

One popular metric for estimating the accuracy of prospective quantitative structure–activity
relationship (QSAR) predictions is based on the similarity of the compound being predicted …

How well can a QSAR model predict the activity of a molecule not in the training set used to
create the model? A set of retrospective cross-validation experiments using 20 diverse in …

Validation is the most crucial concept for development and application of quantitative
structure–activity relationship (QSAR) models. The validation process confirms the reliability …

Abstract Quantitative Structure–Activity Relationship (QSAR) models play a central role in
medicinal chemistry, toxicology and computer‐assisted molecular design, as well as a …

Validation is a crucial aspect of quantitative structure–activity relationship (QSAR) modeling.
The present paper shows that traditionally used validation parameters (leave-one-out Q 2 for …

In the last years, external validation of QSAR models was the subject of intensive debate in
the scientific literature. Different groups have proposed different metrics to find “the best” …

The external prediction capability of quantitative structure− activity relationship (QSAR)
models is often quantified using the predictive squared correlation coefficient, q 2. This index …

This paper deals with the problem of evaluating the predictive ability of QSAR models and
continues the discussion about proper estimates of the predictive ability from an external …