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 …

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

Taking incompatible multiple drugs together may cause adverse interactions and side
effects on the body. Accurate prediction of drug-drug interaction (DDI) events is essential for …

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 …

Drug–drug interactions (DDI) may lead to adverse reactions in human body and accurate
prediction of DDI can mitigate the medical risk. Currently, most of computer-aided DDI …

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 …