Although a wide variety of machine learning (ML) algorithms have been utilized to learn
quantitative structure–activity relationships (QSARs), there is no agreed single best …

Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed to
correlate chemical molecules with their biological activities based on their chemical …

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

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 …

Background Quantitative structure-activity relationship (QSAR) is a computational modeling
method for revealing relationships between structural properties of chemical compounds …

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 …

Emperial methods for building predictive models of the relationships between molecular
stucture and useful properties are becoming increasingly importment. This has arisen …

Feature selection is an important part of contemporary QSAR analysis. In a recently
published paper, we investigated the performance of different feature selection methods in a …

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

Quantitative structure–activity relationship modeling is one of the major computational tools
employed in medicinal chemistry. However, throughout its entire history it has drawn both …