Translating real world and multi omic data into actionable target prioritization remains a significant challenge in AI driven drug discovery. This poster explores how heterogeneous data sources, including clinical real-world data, genomics, transcriptomics, and proteomics, can be integrated into drug design and modeling workflows. Key challenges include data heterogeneity, noise, and alignment with mechanistic models used in design decisions. The discussion will examine how modeling approaches can bridge population level signals with molecular level insights, improve target confidence, and guide downstream design strategies. Emphasis will be placed on model interpretability, validation, and translational relevance.