Drug repositioning is an efficient strategy for drug development and medical treatment, and it has received remarkable attention in pharmaceutical and medical science. The drug repositioning approach can increase the success rate of drug development and to reduce the cost in terms of time, risk, and expenditure. In this study, we developed novel machine learning methods for automatic drug repositioning in order to predict unknown indications of known drugs or drug candidate compounds. The prediction is performed based on the analysis of various large-scale omics data of drugs, compounds, genes, proteins, and diseases in a framework of multi-task learning. Our results show that the proposed method outperforms previous methods in terms of accuracy, applicability, and interpretability. We performed a comprehensive prediction of new indications of all approved drugs and bioactive compounds for a wide range of diseases defined in the International Classification of Diseases. We show several biologically meaningful examples of newly predicted drug indications for cancers and neurodegenerative diseases. The proposed methods are expected to be useful for various applications in drug discovery and medical treatment.

Data-driven drug discovery and medical treatment by machine learning

2019 Fall National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.