A Novel Drug Repositioning Model Based on Heterogeneous Graph Convolutional Network via Multi-task Learning

Compared with traditional methods, drug repositioning is a viable solution to drug discovery. Drug repositioning usually applies the procedure of drug-disease associations (DDAs) prediction, which can reduce the cost and time of drug development and improve the success rate of drug discovery. In this paper, we develop a new multi-task learning framework based on heterogeneous graph convolutional network (MTHGCN) to recognize potential DDAs. In MTHGCN, a heterogeneous information network is constructed by combining multiple biological datasets. And then, a module based on graph convolutional networks is utilized to learn low-dimensional representations of drugs and diseases. Finally, we design two types of auxiliary tasks to help to train the target DDAs prediction task based on the multi-task learning mechanism. We conduct comprehensive experiments on MTHGCN. The results demonstrate the effectiveness of MTHGCN for drug repositioning.