AI-Driven Drug Repurposing

The Opportunity

The FDA has approved approximately 20,000 drugs and compounds. Each was designed for a specific indication, but their full therapeutic potential remains largely unexplored. What if a cancer drug could treat Alzheimer's? What if an antifungal could combat viral infections? The possibilities are vast—and AI can systematically explore them.

Our Approach

We leverage machine learning to analyze massive datasets connecting drugs, diseases, genes, proteins, and biological pathways. By identifying hidden patterns invisible to human researchers, we can predict which existing drugs might effectively treat conditions they were never designed for.

Key Methodologies

• •Network-based approaches: Mapping disease mechanisms and drug effects in high-dimensional biological networks to identify unexpected connections
• •Knowledge graph integration: Combining biomedical literature, clinical trial data, genetic databases, and molecular structures into unified computational frameworks
• •Deep learning for multi-omics: Neural networks trained on genomics, proteomics, and metabolomics data to predict drug-disease interactions
• •Transfer learning: Applying models trained on well-studied diseases to rare conditions with limited data