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

## TL;DR

TargetMap is an AI platform that uses a unified knowledge graph and full-path reasoning to discover drug targets by analyzing biological networks at a system level.

## Contribution

Introduces TargetMap, an AI-powered platform that enables system-level drug target discovery through full-path reasoning on a unified knowledge graph.

## Key findings

- TargetMap uses a unified knowledge graph to facilitate system-level therapeutic target discovery.
- Full-path graph reasoning enables global mechanistic analysis by leveraging LLM capabilities.
- Interactive visualization allows exploration of signaling networks and pathway linearization into temporal narratives.

## Abstract

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.

•Unified knowledge graph facilitates system-level therapeutic target discovery•Full-path graph reasoning unlocks LLM potential for global mechanistic analysis•Interactive visualization enables immersive exploration of signaling networks•Pathway linearization converts topology into coherent temporal narratives

Unified knowledge graph facilitates system-level therapeutic target discovery

Full-path graph reasoning unlocks LLM potential for global mechanistic analysis

Interactive visualization enables immersive exploration of signaling networks

Pathway linearization converts topology into coherent temporal narratives

Applied sciences; Network