AI-powered TargetMap: Enabling system-level target discovery through full-path reasoning on a unified knowledge graph

Modern drug discovery faces a critical challenge: high attrition rates often stem from an incomplete understanding of how individual targets operate within complex, system-wide biological networks. While computational tools such as graph neural network (GNN) excel at extracting local topological patterns, they often fall short in capturing the global semantic context and long-range dependencies within mechanistic pathways. To bridge this gap, we introduce TargetMap, an AI-driven knowledge graph platform powered by an LLM-based full-path graph reasoning algorithm. Our approach leverages the contextual reasoning capabilities of LLMs by representing entire biological pathways-as captured by structured knowledge graphs-as coherent narratives for holistic analysis. This enables the generation of testable hypotheses for distinct therapeutic targets based on a system-level understanding of disease pathology. Supported by a unified knowledge base and Graph RAG, TargetMap provides a paradigm for moving beyond local network analysis to mechanistic reasoning.