A Hybrid Neural Collaborative Filtering Model for Drug Repositioning

Traditional drug development is a time-consuming, high-cost and high-risk process. Computational drug repositioning has become an important strategy to discover new indication for existing drugs due to its remarkable reduction in time, cost and risk. Related studies have indicated that data integration could be helpful to discover novel indications of drugs. However, how to adopt effective system framework to represent and integrate data from different data sources remains a challenging problem. In this study, we propose a computational approach named BioHNCFR, which employs a hybrid neural collaborative filtering framework combined with data integration to predict the potential drug indications. The experimental results on benchmark data set reveal that the performance of our model is improved in predict drug-diseases associations compared to the recent state-of-the-art approaches when evaluated by 10-fold cross validation. The AUC, AUPR on 10-fold cross validation, 93.24%, 39.89%. The HitRate@1 reach to 45.54%. More comprehensive experiments show that our model can predict indications for new drugs.