Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experime...
MOTIVATION: Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug-protein association network and predict DPIs by t...
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are more and more popular in recent y...
MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds...
In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discov...
MOTIVATION: The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the l...
MOTIVATION: Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug-protein interactions mainly focus on ...
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 potential drug interactions. However, most of the available pred...
Drug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning meth...
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.