The Next Drug Target Might Already Be in Your Data

Drug discovery teams often struggle not with data scarcity but with fragmented information spread across genomics, proteomics, pathway databases, compound libraries, assay systems, clinical findings, and scientific literature. Because important target, repurposing, and safety signals frequently emerge from relationships among these sources, researchers must often manually combine data, normalize identifiers, and search across multiple tools, creating inefficiency and increasing the risk of missed evidence. The post argues that traditional relational databases, search systems, and isolated analytics workflows are poorly suited to multi-step scientific questions involving genes, proteins, pathways, diseases, compounds, and clinical context. It presents knowledge graphs as a connection layer that can integrate existing sources while preserving traceability to underlying evidence, citing Cedars-Sinai’s Alzheimer’s Disease Knowledge Base as an example. By making cross-domain relationships easier to explore, connected data systems may help teams identify overlooked targets, drug-repurposing opportunities, and potential safety concerns earlier in development.

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