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 domain literature and internal datasets.

However, unifying isolated proprietary datasets with unstructured open-access repositories still introduces significant data normalisation challenges, requiring strict schema governance to prevent inaccurate relational mapping and mitigate the risk of hallucinations.

Knowledge graph construction

Companies can plug in their own knowledge graphs. The system pulls in messy, unstructured files from public databases like PubMed and mixes them with internal corporate records. Tools like Amazon Comprehend Medical scan this text to pull out standard medical codes. Amazon Bedrock, running Anthropic’s Claude 4.5 Sonnet, summarises the document contents and determines topical relevance.

AWS Lambda functions and Amazon S3 bulk loads then route these processed elements into Amazon Neptune Analytics. The resulting knowledge graph structures the data into discrete nodes representing core entities like domain-specific classes, authors, source journals, and embedded text chunks. The graph edges define the relationships between these nodes, mapping out hierarchical classifications and entity associations. This structured representation provides the deterministic foundation necessary for accurate information retrieval.

The database schema establishes the strict boundaries of the RAG discovery process. Nodes are structured to capture specific conditions and map them hierarchically to established ontologies, while author and journal nodes provide provenance for published research. Lengthy documents are broken down into digestible text segments using Amazon Bedrock Knowledge Base chunking strategies, and specific classification nodes anchor the unstructured textual data to standardised diagnostic metrics.

Operating this graph architecture requires specific cloud resource allocations. A standard Amazon Neptune Analytics graph running with 16 provisioned memory units incurs operational costs of $0.48 per hour….