High-throughput and virtual screening are important components of modern drug discovery
research. Typically, these screening technologies are considered distinct approaches, as …

In this review, we discuss a number of computational methods that have been developed or
adapted for molecule classification and virtual screening (VS) of compound databases. In …

Computational prediction of the interaction between drugs and targets is a standing
challenge in the field of drug discovery. A number of rather accurate predictions were …

The “receiver operating characteristic”(ROC) curve method is a well-recognized metric used
as an objective way to evaluate the ability of a given test to discriminate between two …

The use of Bayesian statistics to model both general (multifamily) and specific (single-target)
kinase inhibitors is investigated. The approach demonstrates an alternative to current …

The rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous
organic compounds is predicted by QSAR modeling. The applied Multiple Linear …

Kinome-wide screening would have the advantage of providing structure–activity
relationships against hundreds of targets simultaneously. Here, we report the generation of …

The kinome-wide virtual profiling of small molecules with high-dimensional structure–activity
data is a challenging task in drug discovery. Here, we present a virtual profiling model …

Quantitative Structure-Activity Relationships (QSARs) are increasingly used to predict the
harmful effects of chemicals to humans and the environment. The increased use of these …

New anxiolytics have been discovered by prediction of biological activity with computer
programs pass and derek for a heterogeneous set of 5494 highly chemically diverse …