MiRAGE: mining relationships for advanced generative evaluation in drug repositioning

Abstract

Motivation: Drug repositioning, the identification of new therapeutic uses for existing drugs, is crucial for accelerating drug discovery and reducing development costs. Some methods rely on heterogeneous networks, which may not fully capture the complex relationships between drugs and diseases. However, integrating diverse biological data sources offers promise for discovering new drug-disease associations (DDAs). Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. However, the challenge lies in effectively integrating different biological data sources to identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms.

Results: In response to this challenge, we present MiRAGE, a novel computational method for drug repositioning. MiRAGE leverages a three-step framework, comprising negative sampling using hard negative mining, classification employing random forest models, and feature selection based on feature importance. We evaluate MiRAGE on multiple benchmark datasets, demonstrating its superiority over state-of-the-art algorithms across various metrics. Notably, MiRAGE consistently outperforms other methods in uncovering novel DDAs. Case studies focusing on Parkinson's disease and schizophrenia showcase MiRAGE's ability to identify top candidate drugs supported by previous studies. Overall, our study underscores MiRAGE's efficacy and versatility as a computational tool for drug repositioning, offering valuable insights for therapeutic discoveries and addressing unmet medical needs.

Keywords: drug repositioning; drug–disease association; feature selection; negative sampling; random forest models; recommender systems.

Figure 1

The scheme of MiRAGE workflow; (a) computing similarity matrices using Jaccard similarity for binary features and BERT similarity for text-based features; (b) utilizing associations mapping to construct sets of containing diseases associated with drug d and containing drugs associated with disease s; (c) obtaining association scoring and adjust association scoring between a drug d and a disease s based on each drug or disease feature, by leveraging the information of and ; (d) constructing a feature vector for a pair by concatenating these scoring and , and , and feeding it to a Random Forest model to predict the association between all drug and disease pairs.