Bomgni, Alain

• Ph.D., Computer Science, University of Picardie Jules Verne

My research focuses on leveraging artificial intelligence and multi-OMICS data integration to revolutionize drug repositioning—the process of finding new uses for existing medications. Building on my previous work in AI-driven pharmacovigilance and machine learning-based drug recommendation systems, this project aims to develop more accurate methods for predicting novel therapeutic applications of approved drugs.

The research applies graph neural networks and deep learning techniques to analyze the complex relationships between drugs, proteins, genes, and metabolic pathways. By integrating proteomics, transcriptomics, and metabolomics data, our approach creates a comprehensive model of drug-target interactions that can identify hidden therapeutic potential.

This work addresses a significant challenge in biomedical research: the high cost and time investment of traditional drug discovery. Our AI-driven approach can accelerate the identification of drug candidates for repurposing, potentially reducing the time and resources needed to bring new treatments to patients. The research has particular relevance for underserved communities, including tribal populations, where access to novel therapeutics may be limited.

The project will produce not only computational models and predictions but also educational resources to train the next generation of researchers in AI-driven drug discovery methods.