FcγRI (CD64) signaling plays a central and pervasive role in a wide range of inflammatory diseases, presenting a significant challenge for lead indication selection due to its broad involvement across multiple pathologies. chAIron developed a data-driven pipeline prioritization strategy to identify the most clinically and commercially viable lead indications. This approach leveraged advanced machine learning algorithms and proprietary knowledge-graph-based biomedical repositories to map the clinical relevance of CD64 expression and activity across human disease datasets. The resulting framework provided a mechanistically informed, evidence-backed rationale for lead indication selection, streamlining downstream development and risk mitigation.