Despite advances in chemical, computational and biological sciences, the rate of attrition of
drug candidates in clinical development is still high. A key point in the small-molecule …

Background The COVID-19 pandemic is a significant public health crisis that is hitting hard
on people's health, well-being, and freedom of movement, and affecting the global economy …

The powerful combination of large-scale drug-related interaction networks and deep
learning provides new opportunities for accelerating the process of drug discovery …

In drug development, unexpected side effects are the main reason for the failure of
candidate drug trials. Discovering potential side effects of drugs in silico can improve the …

Many real-world networks belong to the kind that evolves over time. So it is very meaningful
and challenging to predict whether the link will occur in the network of future time. In this …

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as
both codified data and free-text narrative notes (NLP). The complexity of EHR presents …

In real-world machine learning applications, unlabeled training data are readily available,
but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning …

Learning accurate drug representations is essential for tasks such as computational drug
repositioning and prediction of drug side effects. A drug hierarchy is a valuable source that …

Unexpected side effects may accompany the research stage and post-marketing of drugs.
These accidents lead to drug development failure and even endanger patients' health. Thus …

Amidst prevailing healthcare challenges, a dynamic solution emerges, fusing knowledge
graph technology, clinical trials optimization, dataspace integration, and AI innovation. This …