The precise identification of drug–protein inter action (DPI) can significantly speed up the
drug discovery process. Bioassay methods are time-consuming and expensive to screen for …

The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in
bioinformatics research. Recent advancements in protein structure research have facilitated …

Identification of drug–target interactions (DTIs) is an important step in drug discovery and
drug repositioning. In recent years, graph-based methods have attracted great attention and …

Network biology, an interdisciplinary field at the intersection of computational and biological
sciences, is critical for deepening understanding of cellular functioning and disease. While …

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the
pharmaceutical industry, including drug screening, design, and repurposing. However …

Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed
circular structure. They have been proved to play a significant role in the reduction of many …

Accurately identifying drug-target affinity (DTA) plays a significant role in promoting drug
discovery and has attracted increasing attention in recent years. Exploring appropriate …

In the realm of biomedicine, the prediction of associations between drugs and diseases
holds significant importance. Yet, conventional wet lab experiments often fall short of …

Exploring drug-protein interactions (DPIs) through computational methods can effectively
reduce the workload and the cost of DPI identification. Previous works try to predict DPIs by …

Drug discovery is a crucial aspect of biomedical research, and predicting drug–target
interactions (DTIs) is a vital step in this process. Graph neural networks (GNNs) have …