Fig. 1

DT2Vec+ pipeline. (a-1,2,3) integrating drug–drug (DDS) and protein–protein (PPS) similarity graphs with drug-disease (DDis) and disease-protein (DisP) association graph as input of embedding method to low dimensional vectors. (a-4) Drug–target interaction graph with different edge types. b Graph-embedding developed by node2vec to map nodes to vectors (in this figure, nodes are shown mapped to 2D-vector, x and y). c Known drug–target interactions (six types of interactions) were divided into 10% independent dataset (external testset) and 90% internal test and train (tenfold cross-validation). d Drug and protein vectors were concatenated and labelled using one-hot encoding and an XGBoost model was trained on each label using cross-validation. The best model over the tenfold cross-validation on the internal testset was selected and applied on the external testset. The XGBoost model in c, d was repeated 5 times and the average performance of internal and external testsets was reported