  Drug repositioning is designed to discover new uses of known drugs, which is
an important and efficient method of drug discovery. Researchers only use one
certain type of Collaborative Filtering (CF) models for drug repositioning
currently, like the neighborhood based approaches which are good at mining the
local information contained in few strong drug-disease associations, or the
latent factor based models which are effectively capture the global information
shared by a majority of drug-disease associations. Few researchers have
combined these two types of CF models to derive a hybrid model with the
advantages of both of them. Besides, the cold start problem has always been a
major challenge in the field of computational drug repositioning, which
restricts the inference ability of relevant models. Inspired by the memory
network, we propose the Hybrid Attentional Memory Network (HAMN) model, a deep
architecture combines two classes of CF model in a nonlinear manner. Firstly,
the memory unit and the attention mechanism are combined to generate the
neighborhood contribution representation to capture the local structure of few
strong drug-disease associations. Then a variant version of the autoencoder is
used to extract the latent factor of drugs and diseases to capture the overall
information shared by a majority of drug-disease associations. In that process,
ancillary information of drugs and diseases can help to alleviate the cold
start problem. Finally, in the prediction stage, the neighborhood contribution
representation is combined with the drug latent factor and disease latent
factor to produce the predicted value. Comprehensive experimental results on
two real data sets show that our proposed HAMN model is superior to other
comparison models according to the AUC, AUPR and HR indicators.
