Despite decades of intensive research and a number of demonstrable successes,
quantitative structure-activity relationship (QSAR) models still fail to yield predictions with …

Abstract Quantitative Structure–Activity Relationship (QSAR) models play a central role in
medicinal chemistry, toxicology and computer‐assisted molecular design, as well as a …

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

Quantitative structure–activity relationship (QSAR) models have long been used for making
predictions and data gap filling in diverse fields including medicinal chemistry, predictive …

Background Quantitative structure-activity relationship (QSAR) is a computational modeling
method for revealing relationships between structural properties of chemical compounds …

Carcinogenicity is one of the toxicological endpoints causing the highest concern. Also, the
standard bioassays in rodents used to assess the carcinogenic potential of chemicals and …

Predictability and prediction reliability are of utmost important to characterize a good
Quantitative structure–activity relationships (QSAR) model. However, validation methods are …

Although a number of pitfalls of QSAR have been corrected in the past decade, the reliability
of QSAR models is still insufficient. The reason why QSAR fails is still under hot debate; our …

The predictive accuracy of the model is of the most concern for computational chemists in
quantitative structure− activity relationship (QSAR) investigations. It is hypothesized that the …

Quantitative structure–activity relationships (QSARs) are traditionally implemented using
single models built from small and focused datasets; therefore scalability issues are often …