The human history has witnessed the rapid development of technologies such as high-
throughput sequencing and mass spectrometry that led to the concept of 'omics' and …

Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …

Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …

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 (DDIs) can trigger unexpected pharmacological effects on the body,
and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been …

Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …

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 …

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

Adverse drug–drug interactions (DDIs) have become an increasingly serious problem in the
medical and health system. Recently, the effective application of deep learning and …