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 …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to
effectively treat experimental error in the training and evaluation of computational models. It …

Quantitative structure–activity relationship modeling is one of the major computational tools
employed in medicinal chemistry. However, throughout its entire history it has drawn both …

Given a particular descriptor/method combination, some quantitative structure–activity
relationship (QSAR) datasets are very predictive by random-split cross-validation while …

Numerous chemical data sets have become available for quantitative structure–activity
relationship (QSAR) modeling studies. However, the quality of different data sources may be …

Model reliability is generally assessed and reported as an intrinsic component of
quantitative structure–activity relationship (QSAR) publications; it can be evaluated using …

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

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 …

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 …