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

Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

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

Develo** new drugs remains prohibitively expensive, time-consuming, and often involves
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …

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

One of the main problems with the joint use of multiple drugs is that it may cause adverse
drug interactions and side effects that damage the body. Therefore, it is important to predict …

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 …

The properties of the drug may be altered by the combination, which may cause unexpected
drug–drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs …

Drug–drug interactions (DDIs) for emerging drugs offer possibilities for treating and
alleviating diseases, and accurately predicting these with computational methods can …