Variable selection is important in QSAR modeling since it can improve model performance
and transparency, as well as reduce the computational cost of model fitting and predictions …

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 …

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 …

Variable selection is of crucial significance in QSAR modeling since it increases the model
predictive ability and reduces noise. The selection of the right variables is far more …

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties
were widely used to search lead bioactive molecules in chemical databases. The dataset's …

Background In-silico quantitative structure–activity relationship (QSAR) models based tools
are widely used to screen huge databases of compounds in order to determine the …

In QSAR, a statistical model is generated from a training set of molecules (represented by
chemical descriptors) and their biological activities (an “activity model”). The aim of the field …

Recent implementations of QSAR modelling software provide the user with numerous
models and a wealth of information. In this work, we provide some guidance on how one …

Quantitative structure-activity relationship (QSAR) is a statistical modelling approach that
can be used in drug discovery, environmental fate modeling, property and activity prediction …

It is widely understood that QSAR models greatly improve if more data are used. However,
irrespective of model quality, once chemical structures diverge too far from the initial data …