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

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …

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 …

The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and
result in adverse consequence to the patients. Accurate identification of DDI types can not …

Unexpected drug–drug interactions (DDIs) are important issues for both pharmaceutical
research and clinical applications due to the high risk of causing severe adverse drug …

Expressive molecular representation plays critical roles in researching drug design, while
effective methods are beneficial to learning molecular representations and solving related …

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern
drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively …

Drug–drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with
unknown causal mechanisms. Deep learning methods have been developed to better …