… DDR, which utilizes a heterogeneous drug–target graph that contains information about
various similarities between drugs and similarities between proteins as drug targets. On different …

… , as current methods that predict potential drug–target interactions suffer from high false-…
method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and …

… method DTiGEM (DrugTarget interaction prediction using Graph Embedding and graph
Mining) for … It uses graph embedding, graph-mining, and ML. We evaluate the performance by …

… the target-target similarity and drug-drug similarity graphs, we applied a preprocessing step
by filtering each graph … connecting similar target proteins or similar drugs, the graph will be a …

… drugs (like Aspirin). As a consequence the chances for the development of drugs for target
groups … reduced, since the expected revenue from such drugs is low. In order to improve this …

… , and known drug-protein interactions. A random forest model was trained on extracted …
of the graph built from known DTIs and multiple similarities among the drug-drug and target-target …

… To capture the drug-target interactions and thus identify drug targets, we propose a
SSGCN model that learns the undiscovered correlations between CP-signatures and the …

… graph mining is usually employed at the early stages of drug discovery, it has the potential to
speed up the entire drug discov… Graph mining has also been utilized to study the drug-target …

… a heterogeneous drug-target graph that … drugs and multiple similarities between target
proteins. Both of the methods developed and presented in this study utilize data and graph mining …

… drug–drug and protein–protein similarities to the drug-target bipartite graph to construct the
semi-bipartite graph … geometric distances in drug target nodes and drug–drug and protein–…