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 …

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 …

We present a method to measure the relative importance of the descriptors present in a
QSAR model developed with a computational neural network (CNN). The approach is based …

The central steps in developing QSARs are generation and selection of molecular structure
descriptors and development of the model. Recently, computational neural networks have …

Selecting a small subset of descriptors from a large pool to build a predictive quantitative
structure− activity relationship (QSAR) model is an important step in the QSAR modeling …

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 …

Variable selection k Nearest Neighbor (kNN) QSAR is a popular nonlinear methodology for
building correlation models between chemical descriptors of compounds and biological …

An unsupervised learning method is proposed for variable selection and its performance
assessed using three typical QSAR data sets. The aims of this procedure are to generate a …

Choosing a set of molecular descriptors (features) that is most relevant to a given biological
response variable is a very important problem in QSAR that has not be solved in an optimal …

We improve a recently developed Replacement Method (RM) for the selection of an optimal
set of molecular descriptors from a much greater pool of such regression variables. Our …