Recently, attention mechanism and derived models have gained significant traction in drug
development due to their outstanding performance and interpretability in handling complex …

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 interactions (DDI) are a critical aspect of drug research that can have adverse
effects on patients and can lead to serious consequences. Predicting these events …

Drug side effects have become paramount concerns in drug safety research, ranking as the
fourth leading cause of mortality following cardiovascular diseases, cancer, and infectious …

Abstract Background Drug-drug interactions (DDIs) can result in unexpected
pharmacological outcomes, including adverse drug events, which are crucial for drug …

Subgraph-based methods have proven to be effective and interpretable in predicting drug-
drug interactions (DDIs), which are essential for medical practice and drug development …

Background Discovering potential drug-drug interactions (DDIs) is a long-standing
challenge in clinical treatments and drug developments. Recently, deep learning techniques …

The prediction of drug–drug interactions (DDIs) is essential for the development and
repositioning of new drugs. Meanwhile, they play a vital role in the fields of …

Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal
diseases by analyzing electronic medical records (EMRs), such as notes and veterinary …