DORA: Discovery-Oriented Agent for Drug Repurposing

Scientific discovery requires connecting fragmented evidence to construct and verify hypotheses that are not explicitly documented. We tackle this challenge through a case study on drug repurposing: identifying new therapeutic uses for existing drugs by connecting evidence across molecular targets, pathways, phenotypes, biological processes and disease mechanisms. We construct a comprehensive benchmark from approximately 34k clinical trials and 60k drug–disease pairs. We introduce DORA, a Discovery-Oriented Agent for drug repurposing, guided by an agentic environment-driven paradigm for scientific exploration. We construct a comprehensive biomedical knowledge network consisted of 273K nodes and 16.22M edges from a massive amount of heterogeneous databases, literature, and scientific tools, and design a novel algorithm, Meta-Pathfinder, to uncover non-trivial, implicit mechanistic paths connecting drugs to diseases. Rather than executing a predefined workflow, an LLM operates through a general- purpose coding-agent harness to interact dynamically with this environment: invoking Meta-Pathfinder and scientific tools, traversing evidence, identifying missing links, challenging competing hypotheses, and iteratively refining therapeutic hypotheses. Across seven frontier models, DORA improves five LLM-only baselines, with gains of 2.45–28.09 normalized-score points and 1.00–16.00 percentage points in categorical accuracy; two backbones do not improve. Notably, smaller models equipped with our agentic environment can outperform substantially larger models without it. To our knowledge, this is the first systematic framework for agentic drug repurposing, moving beyond retrieving and reasoning over existing knowledge to connecting fragmented evidence, uncovering latent mechanisms, and generating novel therapeutic hypotheses. We will publicly release our platform, data, and resources to support reproducible research.