AWS GraphRAG deployment cuts drug research cycles by 87%

A recent AWS GraphRAG deployment reduced drug research and development cycles in pharmaceutical environments by 87 percent. This acceleration is achieved by integrating previously separated proprietary databases into a unified and queryable knowledge graph.

Historically, initial data gathering and screening phases took over six months per iteration, yielding a low five percent success rate. Crucial datasets – ranging from domain-specific clinical metrics to internal engineering and laboratory notes – were isolated across storage environments, effectively blocking data scientists from uncovering latent correlations. When staff left, they took crucial project context with them, stalling active research.

AWS built a solution to connect these systems, combining graph databases with NLP.

The setup relies on a GraphRAG framework and uses Amazon Neptune Analytics and Bedrock to turn disconnected data points into a searchable network. Users can submit standard natural language queries and receive answers mapped to verified do...