Virtual screening (VS) has emerged in drug discovery as a powerful computational
approach to screen large libraries of small molecules for new hits with desired properties …

The scientists, and the researchers around the globe generate tremendous amount of
information everyday; for instance, so far more than 74 million molecules are registered in …

Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied
as an evaluative approach, ie, with the focus on developing retrospective and explanatory …

Highlights•Six commonly used machine learning methods in QSAR models are
summarized.•Newly developed combinatorial QSAR and hybrid QSAR methods are …

Introduction: Quantitative structure–activity relationship (QSAR) modeling is one of the most
popular computer-aided tools employed in medicinal chemistry for drug discovery and lead …

Quantitative structure-activity relationship (QSAR) modeling is themajor cheminformatics
approach to exploring and exploiting the dependency of chemical, biological, toxicological …

After nearly five decades “in the making”, QSAR modeling has established itself as one of
the major computational molecular modeling methodologies. As any mature research …

Highlights•A few newly introduced molecular descriptors were discussed.•Various
computational approaches to calculate the descriptors are listed.•We described several …

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

Virtual filtering and screening of combinatorial libraries have recently gained attention as
methods complementing the high-throughput screening and combinatorial chemistry. These …