Challenge:
Cancer remains one of the most heterogeneous diseases, with patients showing diverse molecular and phenotypic responses to the same therapy. This variability makes it difficult to identify effective drug targets and predict treatment outcomes across individuals.

Why it matters: A “one-size-fits-all” approach to cancer treatment leads to suboptimal responses and drug resistance. Understanding patient-specific molecular profiles and drug–target interactions is essential for tailoring precise therapies and identifying opportunities for drug repurposing—especially when existing compounds could be redirected to novel cancer subtypes.

Current status:
Recent advances in multi-omics profiling and AI-based drug discovery have shown promise in modeling drug–gene interactions. However, these methods often overlook tumor heterogeneity, dynamic signaling rewiring, and population diversity, which are key to predicting individualized drug responses.

Aim:
Develop an AI-driven framework that integrates multi-omics, structural biology, and pharmacogenomic data to model tumor heterogeneity and predict patient-specific drug suitability and repurposing opportunities. By combining deep learning architectures (transformers, GNNs, and multimodal fusion networks), the system will learn cross-scale representations linking mutation profiles, protein structures, and drug mechanisms. This approach will:

· Identify repurposable drugs for resistant or rare cancer subtypes.
· Stratify patients based on molecular signatures and predicted therapeutic response.
· Accelerate precision oncology pipelines by reducing experimental screening costs.

Impact:
This research will enable data-driven, individualized cancer treatment, transforming how therapies are matched to patients and opening new pathways for targeting cancer heterogeneity with repurposed or novel drugs.