Fig. 1. A diagram illustrating the workflow of deepDTnet. DeepDTnet embeds the 15 types of chemical, genomic, phenotypic, and cellular networks and applies a deep neural network algorithm to learn a low-dimensional vector representation of the features for each node (see ESI Methods†). After learning the feature matrix X and Y for drugs and targets (i.e., each row in X and Y represents the feature vector of a drug or a target, respectively), deepDTnet applies PU-matrix completion to find the best projection from the drug space onto target (protein) space, such that the projected feature vectors of drugs are geometrically close to the feature vectors of their known interacting targets. Finally, deepDTnet infers new targets for a drug ranked by geometric proximity to the projected feature vector of the drug in the projected space (see Methods).