Powering scientific discovery: BYOKG and GraphRAG for intelligent pharmaceutical research

In pharmaceutical research, scientists face a fundamental challenge: accessing and connecting the vast amount of scientific knowledge scattered across disparate systems. From published literature and internal lab notes to genomics databases, critical insights remain trapped in silos, making it difficult for researchers to form comprehensive connections and generate promising hypotheses. This fragmentation slows down the drug discovery process. It also risks valuable institutional knowledge being lost as researchers transition, ultimately affecting the industry’s ability to research and develop efficiently. The need for a solution that can intelligently bridge these knowledge gaps while maintaining scientific integrity has become increasingly important.

The challenge: Scattered data across fragmented systems

At leading pharmaceutical companies, researchers face a critical challenge in early-stage drug discovery, where traditional methods yield only a 5 percent success rate and initial screening takes over six months. Scientists struggle to connect insights buried across fragmented systems such as PubMed, internal lab notes, and genomics databases, all while racing against competitors and time constraints. The scattered nature of data leads to redundant work and missed opportunities. It also makes it difficult to trace the evidence trail needed for regulatory approval. When researchers depart, they often take valuable tacit knowledge with them, further compromising the institutional memory needed for breakthrough discoveries.

Challenges in early-stage drug discovery:

These challenges collectively create a significant bottleneck in the drug discovery pipeline, leading to inefficiencies, missed opportunities, and potential delays in developing life-saving treatments. Our solution addresses these bottlenecks by moving beyond traditional methods: graph-powered AI supports pharmaceutical research by creating an interconnected knowledge environment. Using Amazon Neptune Analytics, researchers can now ask complex questions in natural language and receive instant, evidence-backed insights drawn from a unified knowledge graph that connects everything from compound interactions to gene expressions and clinical studies. This approach doesn’t only provide answers. It reveals the complete reasoning behind each result by showing detailed citation paths and graph traversal steps. By exposing how the system navigates through interconnected research papers and data points, it makes scientific discovery more transparent and reproducible.

By combining graph and generative AI, research scientists don’t only retrieve information. They can amplify reasoning, preserve institutional memory, and surface insights that would otherwise stay buried. It also helps them generate better hypotheses, move faster, and trust the outputs, because every insight comes with context and proof. In a field where the cost of delay is measured in both dollars and lives, this shift is more than helpful. It changes how research gets done.

In this post, we explore how Graph-based Retrieval Augmented Generation (GraphRAG) is transforming scientific research by combining graph databases with generative AI. With this approach, you can accelerate discovery processes without compromising scientific integrity.

By integrating Amazon Neptune Analytics for high-performance graph processing with Amazon Bedrock, researchers can build sophisticated systems that not only understand complex scientific relationships but also provide intuitive natural language interfaces. The GraphRAG architecture helps enhance the quality of AI-generated responses by intelligently traversing knowledge graphs to identify relevant information paths. This makes sure that the responses are firmly anchored in verified scientific data.

What makes this solution powerful for scientific research is its ability to understand and connect intricate relationships between entities, from plants and compounds to proteins, genes, and their associated health effects. With this comprehensive understanding, researchers can uncover insights more efficiently and make data-driven decisions with greater confidence.

The solution reimagines the research process through a Bring Your Own Knowledge Graph (BYOKG) approach enhanced with GraphRAG capabilities. A knowledge graph is a structured representation of information that shows relationships between different entities as a network of interconnected nodes and edges. Powered by Amazon Neptune, it integrates diverse scientific entities (plants, compounds, genes, proteins, and health effects) into a unified knowledge network that bridges data from public sources like PubMed and Gene Ontology with proprietary datasets. Automated ingestion pipelines and graph algorithms continuously enrich the graph, helping researchers uncover complex biological relationships and insights that were previously hidden across disconnected data silos.

Using Neptune Analytics and Amazon Bedrock, the solution combines graph algorithms with natural language querying to make scientific exploration both analytical and intuitive. Researchers can ask complex questions in plain English and receive evidence-based answers derived from graph traversal, complete with source citations and visual pathways. Interactive visualization tools further help enhance transparency and understanding, allowing users to explore relationships, trace hypotheses to conclusions, and validate results with clear, verifiable evidence. This accelerates discovery and strengthens scientific rigor across domains.

Our solution helps researchers quickly discover relevant medical journal articles across conditions and topics. The dataset includes the HCLS journal articles provided by the PMC Open Access Subset licensed with CC BY and CC0 licenses, journal metadata provided by the National Center for Biotechnology Information (NCBI) via the Bio.Entrez package, Disease Ontology hierarchies, and ICD10 codes that have been extracted using the ICD-10-CM linking API within Amazon Comprehend Medical. Although the final dataset is provided to you, the following architecture depicts the flow used to create the dataset.

The following diagram illustrates the loading of the data to Amazon Neptune Analytics using services like Amazon Bedrock and Amazon Comprehend to extract data from medical journals.

The following image represents the final graph, which contains these node types:

Because we’re using our own graph data model, we use the BYOKG-RAG toolkit to implement natural language querying over the graph. The following diagram illustrates the components of BYOKG.

Before getting started, make sure you have the following prerequisites:

Cost approximation (per hour) for running this demo:

Let’s begin implementing your GraphRAG solution by setting up Neptune Analytics. The following steps will guide you through data import, graph creation, and notebook configuration to build your knowledge graph foundation:

Implementation steps: Building a modular GraphRAG system with the GraphRAG Toolkit and Amazon Bedrock

In this notebook, we demonstrate a modular approach to building a Retrieval Augmented Generation (RAG) system over a healthcare knowledge graph, using the graphrag-toolkit Python package and the Amazon Bedrock Anthropic Claude 4.5 Sonnet model. This solution supports natural language querying and entity linking within a knowledge graph, combining advanced language model generation with structured graph data retrieval.

This modular structure cleanly separates the following components:

The modular structure allows flexible experimentation and straightforward extension for different domains or datasets.

The integration of a fuzzy string matcher facilitates robust entity recognition, which is important in noisy or complex healthcare data contexts.

By combining the Amazon Bedrock advanced language models with structured graph querying and linking, this solution forms a powerful foundation for context-aware question answering and information retrieval over knowledge graphs.

Solution benefits and performance metrics

The following key performance indicators from our implementation of the solution demonstrate how this GraphRAG solution can create measurable value and competitive advantage for pharmaceutical research organizations:

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The integration of GraphRAG technology with Amazon Neptune Analytics and Amazon Bedrock represents a significant advancement in scientific research methodology. Researchers can now connect previously siloed data sources, interact with complex datasets using natural language queries, and visualize intricate relationships. This solution can deliver immediate, measurable impact for research organizations by reducing research cycle times by up to 87 percent and increasing discovery hit rates five-fold. It not only accelerates the pace of discovery but also helps enhance the quality and credibility of scientific findings. This solution supports rapid scientific advancements, potentially leading to outcomes that were unattainable within traditional research timeframes. Organizations that adopt generative AI solutions are not only improving their research processes. They are positioning themselves at the forefront of scientific innovation, ready to tackle the most complex challenges of our time with greater speed and accuracy.