Drug-target Interaction Prediction via Multiple Output Deep Learning

Computational prediction of drug-target interaction (DTI) is very important for the new drug discovery. However, by connecting drugs and targets to form drug target pairs, the number of interactions is limit, most interactions focus on only a few targets or a few drugs, and the number of drug target pairs is far more than the number of interactions, which causes to be over fitting. To overcome the above problem, in this paper, a multiple output deep neural network (MODNN) based DTI prediction is designed. MODNN enhances its learning ability with a kind of auxiliary classifier layers. The parameters used in the training process are elaborated from the auxiliary and main classifier layers, which can increase the gradient signal that gets propagated back, utilize multi- level features to train the model, and use the features produced by the higher, middle or lower layers in a unified framework. The conducted experiments validate the effectiveness of our MODNN.