Drug discovery cycles slashed 87% — and it's thanks to graphs
aws graphrag cut drug development cycles by 87% by unifying proprietary databases, signaling a shift from vector embeddings to structured graphs for high-stakes r&d.
a pharmaceutical deployment using aws graphrag reduced drug development cycles by 87% by integrating proprietary databases into a unified knowledge graph. the system replaced a six-month, low-yield vector search process with a graph-based approach that connects previously siloed data points, significantly accelerating the initial screening phase. this adoption signals that enterprise r&d is moving beyond basic embeddings to graph-based retrieval augmented generation to handle complex, private data. the shift suggests that while vector databases excel at semantic search, knowledge graphs are superior for reasoning over interconnected scientific data.