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Powering scientific discovery

Ch06.042 Powering scientific discovery

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Powering scientific discovery: BYOKG and GraphRAG for intelligent pharmaceutical research

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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:

  1. Poor success rate and time efficiency – Only 5 percent hit rate with over 6 months of screening time per attempt.
  2. Fragmented knowledge systems – Critical insights scattered across PubMed, lab notes, and databases, leading to missed connections.
  3. Loss of institutional memory – Valuable knowledge disappears when researchers leave, breaking continuity in research efforts.

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 exp

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