Computational analysis of molecular pathways across 547 genetic disorders revealed 89 previously unrecognized druggable protein nodes that could serve as therapeutic targets for multiple rare diseases simultaneously. The AI platform identified convergent cellular mechanisms where seemingly unrelated genetic conditions share downstream molecular dysfunction, particularly in protein folding, mitochondrial metabolism, and autophagy pathways. This approach represents a fundamental shift from the traditional one-gene-one-drug paradigm toward network-based therapeutics. The implications extend beyond rare disease treatment to personalized medicine generally, as the same computational framework could identify drug repurposing opportunities for common complex diseases. However, the challenge lies in translating these computational predictions into actual clinical benefit—a gap that has historically proven difficult to bridge in precision medicine. The platform's ability to prioritize targets based on existing drug libraries could accelerate development timelines significantly, potentially reducing the 10-15 year average for rare disease drug development. Most intriguingly, several identified targets showed therapeutic potential across neurological, metabolic, and connective tissue disorders, suggesting fundamental biological processes that could be modulated to treat multiple conditions with single interventions.