Predicting drug-target interactions (DTIs) is crucial at many phases of drug discovery and repositioning. Many computational methods based on heterogeneous networks (HNs) have proved their potential to predict DTIs by capturing extensive biological k...
MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to exp...
The way of co-administration of drugs is a sensible strategy for treating complex diseases efficiently. Because of existing massive unknown interactions among drugs, predicting potential adverse drug-drug interactions (DDIs) accurately is promotive t...
MOTIVATION: Drug-drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificia...
Drug-drug interactions (DDIs) are known as the main cause of life-threatening adverse events, and their identification is a key task in drug development. Existing computational algorithms mainly solve this problem by using advanced representation lea...
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...