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 …

In this work, we report the development of a novel QSAR technique combining genetic
algorithms and neural networks for selecting a subset of relevant descriptors and building …

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 …

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 …

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 …

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

Descriptor selection in QSAR typically relies on a set of upfront working hypotheses in order
to boil down the initial descriptor set to a tractable size. Stepwise regression …

In the past decade, many authors replaced multivariate regression (MR) by the neural
networks (NNs) algorithm because they believed the latter to be superior. To verify this, we …

Although artificial neural networks have occasionally been used for Quantitative Structure-
Activity/Property Relationship (QSAR/QSPR) studies in the past, the literature has of late …

Machine learning has a vast range of applications. In particular, advanced machine learning
methods are routinely and increasingly used in quantitative structure activity relationship …