The integration of machine learning and structure-based methods has proven valuable in
the past as a way to prioritize targets and compounds in early drug discovery. In oncological …

The drug discovery paradigm has been very time-consuming, challenging, and expensive;
however, the disease conditions originating from bacteria, virus, protozoa, fungus and other …

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic
targets. Membrane proteins are key players in various cancer types but present unique …

Although the literature contains numerous examples of successful virtual screening
campaigns, the field continues to evolve and deal with new challenges. In this special issue …

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity
prediction of kinase inhibitors. A number of models based on available datasets can be used …

Proteochemometric (PCM) modelling is the new way of developing quantitative structure
activity relationship models. It is computational method in which multiple ligands and …

Bioactivity prediction is essential in computational drug discovery, particularly within virtual
screening campaigns. Despite advancements in model architectures and features, the …