Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR
RP Sheridan, BP Feuston, VN Maiorov… - Journal of chemical …, 2004 - ACS Publications
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 …
create the model? A set of retrospective cross-validation experiments using 20 diverse in …
Three useful dimensions for domain applicability in QSAR models using random forest
RP Sheridan - Journal of chemical information and modeling, 2012 - ACS Publications
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 …
relationship (QSAR) predictions is based on the similarity of the compound being predicted …
The effect of noise on the predictive limit of QSAR models
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 …
effectively treat experimental error in the training and evaluation of computational models. It …
Using random forest to model the domain applicability of another random forest model
RP Sheridan - Journal of chemical information and modeling, 2013 - ACS Publications
In QSAR, a statistical model is generated from a training set of molecules (represented by
chemical descriptors) and their biological activities. We will call this traditional type of QSAR …
chemical descriptors) and their biological activities. We will call this traditional type of QSAR …
Determining the validity of a QSAR model− a classification approach
R Guha, PC Jurs - Journal of chemical information and modeling, 2005 - ACS Publications
The determination of the validity of a QSAR model when applied to new compounds is an
important concern in the field of QSAR and QSPR modeling. Various scoring techniques can …
important concern in the field of QSAR and QSPR modeling. Various scoring techniques can …
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
A Golbraikh, A Tropsha - Molecular diversity, 2000 - Springer
One of the most important characteristics of Quantitative Structure ActivityRelashionships
(QSAR) models is their predictive power. The latter can bedefined as the ability of a model to …
(QSAR) models is their predictive power. The latter can bedefined as the ability of a model to …
Assessing the reliability of a QSAR model's predictions
L He, PC Jurs - Journal of Molecular Graphics and Modelling, 2005 - Elsevier
Quantitative structure activity relationships (QSAR) are one of the well-developed areas in
computational chemistry. In this field, many successful predictive models have been …
computational chemistry. In this field, many successful predictive models have been …
On outliers and activity cliffs why QSAR often disappoints
GM Maggiora - Journal of chemical information and modeling, 2006 - ACS Publications
Quantitative structure-activity relationships (QSAR) have been around for many years and
have been employed in numerous fields from drug design to environmental toxicology …
have been employed in numerous fields from drug design to environmental toxicology …
QSAR− how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets
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 …
a large number of factors including the descriptor set, the statistical method, and the data …
How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?
One of the important applications of quantitative structure-activity relationship (QSAR)
models is to fill data gaps by predicting a given response property or activity from known …
models is to fill data gaps by predicting a given response property or activity from known …