Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …

In the fields of pharmaceutical research and biomedical sciences, QSAR modeling is an
established approach during drug discovery for prediction of biological activity of drug …

Since the advent of QSAR (quantitative structure activity relationship) modeling quantitative
representations of molecular structures are encoded in terms of information-preserving …

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

Quantitative structure–activity relationship (QSAR) modelling is currently used in multiple
fields to relate structural properties of compounds to their biological activities. This technique …

Quantitative structure–activity relationship (QSAR) models are routinely applied
computational tools in the drug discovery process. QSAR models are regression or …

Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are
statistical models which quantitatively correlate quantitative chemical structure information …

Molecular latent representations, derived from autoencoders (AEs), have been widely used
for drug or material discovery over the past couple of years. In particular, a variety of …

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 selection of the most relevant molecular descriptors to describe a target variable in the
context of QSAR (Quantitative Structure‐Activity Relationship) modelling is a challenging …