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 …

Neural networks (NNs) are accepted as the most powerful nonlinear technique in QSAR and
QSPR modeling. However, the NN models are often very robust, containing a large number …

In this study we want to test whether a simple modeling procedure used in the field of
QSAR/QSPR can produce simple models that will be, at the same time, as accurate as …

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 Probabilistic Neural Network (PNN) and its close relative, the Generalized Regression
Neural Network (GRNN), are presented as simple yet powerful neural network techniques …

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 …

Support vector machines (SVMs) were used to develop QSAR models that correlate
molecular structures to their toxicity and bioactivities. The performance and predictive ability …

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

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

Based on the quantitative structure‐activity relationships (QSARs) models developed by
artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable‐selection …