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

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 …

Deep learning has achieved impressive results in various fields such as computer vision
and natural language processing, making it a powerful tool in biology. Its applications now …

Graph neural networks (GNNs), which are capable of learning representations from
graphical data, are naturally suitable for modeling molecular systems. This review …

Drug–drug interactions (DDIs) are a major concern in clinical practice and have been
recognized as one of the key threats to public health. To address such a critical threat, many …

Drug-drug interactions can produce unpredictable pharmacological effects and lead to
adverse events that have the potential to cause irreversible organ damage or death …

Polypharmacy, the co-administration of multiple drugs, has become an area of concern as
the elderly population grows and an unexpected infection, such as COVID-19 pandemic …

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 …

Deep learning-based approaches have achieved promising performance in predicting drug–
drug interactions (DDIs). Nevertheless, a significant drawback of these approaches is their …

Accurate prediction of drug-drug interaction (DDI) is crucial to achieving effective decision-
making in medical treatment for both doctors and patients. Recently, many deep learning …