Abstract Machine Learning for ligand based virtual screening (LB-VS) is an important in-
silico tool for discovering new drugs in a faster and cost-effective manner, especially for …

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges
as a transformative force, bridging the realms of biology and technology. This paper …

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

Refined nearest neighbor analysis was recently introduced for the analysis of virtual
screening benchmark data sets. It constitutes a technique from the field of spatial statistics …

Lead discovery is currently a key bottleneck in the pipeline for much-needed novel drugs for
tropical diseases such as malaria, tuberculosis, African sleeping sickness, leishmaniasis …

Highlights•This review provides an overview of the theory and applications of active learning
in drug discovery.•The active learning concept enables economic compound screening.•We …

Computational techniques that build models to correctly assign chemical compounds to
various classes of interest have many applications in pharmaceutical research and are used …

Quantitative structure-activity relationships (QSARs) have important applications in drug
discovery research, environmental fate modeling, property prediction, etc. Validation has …

The success of ligand-based virtual-screening calculations is influenced highly by the nature
of target-specific structure–activity relationships. This might pose severe constraints on the …

Some small molecules, often hits from screening, form aggregates in solution that inhibit
many enzymes. In contrast, drugs are thought to act specifically. To investigate this …