A computational drug repositioning model based on hybrid similarity side information powered graph neural network

Computational drug repositioning technology aims to rediscover the potential use of drugs already on the market and can significantly accelerate the traditional drug development process, reducing significant drug development costs and drug development instability In this work, in order to capture valid and robust hidden feature representations of drugs and diseases, we introduce a new computational drug relocation model, HSSIGNN, based on hybrid similarity side information powered graph neural network, by drawing on the application of graph neural networks and Side information in recommender systems. Its advantage is to utilize the learning capability of graph neural networks to capture the effective hidden feature representation of drugs and diseases, which is used to infer the probability of whether a drug can treat the disease of interest, as a way to improve the generalization capability of the model. In addition, dimensionality reduction algorithms and side information of drugs and diseases are used to overcome the cold start problem encountered by traditional computational drug relocation models. Finally, the experimental results of the proposed model on two real drug-disease association datasets are analyzed to verify its superiority and effectiveness. Comprehensive experimentations on several real-world datasets show the efficiency of HSSIGNN. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Computational drug repositioning technology aims to rediscover the potential use of drugs already on the market, accelerate the traditional drug development process, and reduce costs and instability. The new HSSIGNN model utilizes graph neural networks and side information to capture effective hidden feature representations of drugs and diseases, improving the model's generalization capability.