Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …

Predicting the side effects caused by drug combinations may facilitate the prescription of
multiple medications in a clinical setting. So far, several prediction models of multidrug side …

Capturing comprehensive information about drug–drug interactions (DDIs) is one of the key
tasks in public health and drug development. Recently, graph neural networks (GNNs) have …

Background Breast cancer is currently one of the cancers with a higher mortality rate in the
world. The biological research on anti-breast cancer drugs focuses on the activity of …

Signed network graphs provide a way to model complex relationships and
interdependencies between entities: negative edges allow for a deeper study of social …

Structural balance modeling for signed graph networks presents how to model the sources
of conflicts. The state-of-the-art has focused on computing the frustration index of a signed …

Complex interactions between biology entities (drugs, diseases, side-effects, etc.), have
posed difficulties for drug discovery and treatment. Despite the significant efforts that have …

Drug drug interactions (DDIs) are crucial for drug research and pharmacologia. Recently,
graph neural networks (GNNs) have handled these interactions successfully and shown …

Prediction of drug-drug interactions (DDI) is a new area of research in the field of
pharmaceuticals. The main goal of these studies is to minimize risks for the patient caused …