Quantitative structure-activity relationships (QSAR) have been around for many years and
have been employed in numerous fields from drug design to environmental toxicology …

Quantitative structure—activity relationships (QSARs) attempt to correlate chemical structure
with activity using statistical approaches. The QSAR models are useful for various purposes …

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 …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied
as an evaluative approach, ie, with the focus on developing retrospective and explanatory …

Abstract Interpretation of QSAR models is useful to understand the complex nature of
biological or physicochemical processes, guide structural optimization or perform …

The reliability of any quantitative structure–activity relationship (QSAR) model depends on
multiple aspects such as the accuracy of the input dataset, selection of significant …

A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure
attributes (molecular descriptors) to a biological activity. QSAR studies have now become …

There are no formal guidelines for the development of quantitative structure–activity
relationships (QSARs). However, there are a number of practices that should be avoided …

Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are
statistical models which quantitatively correlate quantitative chemical structure information …