Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …

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

Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …

Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently,
deep learning (DL) models for show promising performance for DTI prediction. However …

A major concern with co-administration of different drugs is the high risk of interference
between their mechanisms of action, known as adverse drug–drug interactions (DDIs) …

Drug–drug interaction (DDI) prediction identifies interactions of drug combinations in which
the adverse side effects caused by the physicochemical incompatibility have attracted much …

Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly
cause morbidity and mortality. Thus, the identification of potential DDIs is essential for …

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …

How to produce expressive molecular representations is a fundamental challenge in
artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a …

Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …