Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Objective To summarize applications of natural language processing (NLP) in model
informed drug development (MIDD) and identify potential areas of improvement. Materials …

Computational drug repurposing aims to identify new indications for existing drugs by
utilizing high-throughput data, often in the form of biomedical knowledge graphs. However …

Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …

Prediction of the drug–target affinity (DTA) plays an important role in drug discovery. Existing
deep learning methods for DTA prediction typically leverage a single modality, namely …

Accurate identification of potential drug–target interactions (DTIs) is a crucial task in drug
development and repositioning. Despite the remarkable progress achieved in recent years …

Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and
intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to …

Over the last decade, for the first time, substantial efforts have been directed at the
development of dedicated in silico platforms for drug repurposing, including initiatives …

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately,
applications of GPCRs in cancer therapy are scarce due to very limited knowledge …

With the development of research on the complex aetiology of many diseases,
computational drug repositioning methodology has proven to be a shortcut to costly and …