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 discovery and development affects various aspects of human health and dramatically
impacts the pharmaceutical market. However, investments in a new drug often go …

Developing personalized diagnostic strategies and targeted treatments requires a deep
understanding of disease biology and the ability to dissect the relationship between …

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various
areas, such as virtual screening, drug repurposing and identification of potential drug side …

Motivation Drug combination therapy has become an increasingly promising method in the
treatment of cancer. However, the number of possible drug combinations is so huge that it is …

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data
has offered unprecedented opportunities for drug discovery including drug repurposing …

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have
been in use in a variety of intelligent medical applications. Thus, understanding the research …

Drug discovery and development is a complex and costly process. Machine learning
approaches are being investigated to help improve the effectiveness and speed of multiple …

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 …

Biomedical knowledge graphs (KGs) constructed from medical literature have been widely
used to validate biomedical discoveries and generate new hypotheses. Recently, large …