Accelerating Drug Repurposing and Target Discovery using Deep Learning

Drug discovery is a long-lasting and expensive process. Computational approaches, particularly deep learning techniques, offer the potential to accelerate this process by integrating diverse perspectives from drug discovery theories and capturing intricate patterns within large, multimodal datasets. This dissertation explores deep learning methodologies to accelerate drug repurposing and genetic target discovery. The first aim focuses on integrating multimodal data, including chemical structures, disease genetics, and systems biology, into comprehensive disease knowledge graphs, followed by applying graph neural networks (GNNs) to prioritize repurposable drug candidates. The second aim is to develop a heterogeneous GNN-based approach capable of modelling distinct semantic relationships within complex disease knowledge graphs, achieving superior performance compared to baseline models in identifying repurposable drugs. The third aim presents a transformer-based framework to predict glioma patient recurrence status and identify potential genetic targets using patient multi-omics data. By combining medical, genomic, and chemical data with deep learning techniques, this dissertation introduces innovative frameworks to expedite multi-stage drug discovery, facilitating the identification of repurposable drugs and promising genetic targets for therapeutic development.