MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction

Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the probability of finding new interactions by reducing the cost of in vitro experiments. In this paper, a Meta-path-based Representation Learning model, namely MRLDTI, is proposed to predict unknown DTIs. Specifically, we first design a random walk strategy with a meta-path to collect the biological relations of drugs and targets. Then, the representations of drugs and targets are captured by a heterogeneous skip-gram algorithm. Finally, a machine learning classifier is employed by MRLDTI to discover novel DTIs. Experimental results indicate that MRLDTI performs better than several state-of-the-art models under ten-fold cross-validation on the gold standard dataset.