Deep Probabilistic Matrix Factorization on Graphs: Application to Drug Repositioning in Antimicrobial Resistance

Antimicrobial resistance (AMR) is a significant global health challenge caused by the misuse and overuse of antibiotics in various sectors, leading to the development of resistant bacteria. In such infections, the first-line antibiotics intended for specific diseases become ineffective, necessitating the repurposing of other antibiotics for treatment. To address this, we have developed a new algorithm for general-purpose drug repositioning based on a matrix completion framework on graphs. Our probabilistic approach combines deep matrix factorization with graph learning to achieve precise drug repurposing. In this study, we curated a new dataset on antibiotic-bacteria associations. Applying our proposed method to this dataset demonstrates that our approach outperforms benchmarks in both general-purpose drug repositioning and three specific AMR case studies.