AI-Driven Knowledge Graph Models for Drug Repurposing and Precision Medicine

Music City Center 

Drug repurposing, the process of identifying new applications for existing approved drugs, offers a time- and cost-efficient approach to drug development. The explosive growth of biomedical data provides significant opportunities to advance drug repurposing and precision medicine. However, effectively integrating complex, heterogeneous data to uncover meaningful repurposing signals remains challenging. In this presentation, I will introduce our research group's work on AI-driven knowledge graph models, which systematically integrate genomic, phenotypic, pharmacological and patient data and leverage deep learning algorithms to identify candidate drugs for repurposing and personalized treatment strategies. Through case studies, I will illustrate the application of our approach in uncovering potential therapeutic signals and enabling personalized medicine.