Multi-task learning (MTL) is a machine learning paradigm that aims to enhance the
generalization of predictive models by leveraging shared information across multiple tasks …

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 …

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 …

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

Drug similarity has been studied to support downstream clinical tasks such as inferring novel
properties of drugs (eg side effects, indications, interactions) from known properties. The …

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance,
which provides effective and safe co-prescriptions of multiple drugs. Since laboratory …

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
Identifying potential DDIs during the drug design process is critical for patients and society …

Drug-drug interactions (DDIs) could lead to various unexpected adverse consequences, so-
called DDI events. Predicting DDI events can reduce the potential risk of combinatorial …

Drug–drug interactions are one of the major concerns of drug discovery, and the accurate
prediction of drug–drug interactions is important for drug safety surveillance. However, most …

Motivation Drug–drug interaction (DDI) prediction is a challenging problem in pharmacology
and clinical applications. With the increasing availability of large biomedical databases …