A production deployment of Graph Retrieval-Augmented Generation on AWS cut pharmaceutical research cycles from six months to three weeks, yielding an 87% efficiency gain and quintupling screening hit rates. AWS published the full architecture and performance metrics on July 8, 2026 in a technical post on its Machine Learning Blog, authored by a four-person team. The post documents that early-stage drug discovery has historically achieved only a 5% success rate while consuming more than six months of researcher time per iteration. The reason is architectural, as researchers work across systems that were never designed to communicate, including PubMed, NCBI databases, proprietary systems, and ICD-10 diagnostic codes. Traditional vector-based RAG systems retrieve semantically similar text passages but cannot perform multi-hop reasoning, such as following a chain from compound X binding protein Y to pathway Z to a cardiovascular disease mechanism. The AWS deployment addresses this by building a unified knowledge graph that encodes not just documents but the relationships between the entities those documents describe.