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 …

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 …

A quantitative structure–activity relationship (QSAR) is a model relating a specific biological
response to the chemical structures of compounds. There are many descriptor sets available …

Feature selection is one of the most commonly used and reliable methods for deriving
predictive quantitative structure–activity relationships (QSAR). Many feature selection …

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 …

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 …

The quality of QSAR (Quantitative Structure− Activity Relationships) predictions depends on
a large number of factors including the descriptor set, the statistical method, and the data …

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 …

Validation Criteria of QSAR Models.......................................... 214 7.3 Validation of QSAR
Models: Y-Randomization.............................. 220 7.4 Validation of QSAR Models: Training …

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 …