Enterprise FAIR KGs for Drug Development
KGC 2026
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38m
We report on an initiative that operationalizes FAIR data principles to transform Chemistry Manufacturing and Controls (CMC) data into reusable, interoperable data products. The program delivers an ontology‑driven semantic layer and automated pipelines that decouple data sources from consumers, enabling consistent, high‑quality access to cross‑domain drug development data at scale. We integrated five use cases, spanning batch history, bio drug substance to drug product interactions, lot genealogy, and formulation stability for deployment in Neo4j.
We will present on our knowledge engineering and governance processes for ontologies and data mappings; use of standardized ontologies, vocabularies, and SHACL rules; semantic anchoring in RDF for all data; data mappings using semantic data dictionaries; and a CI/CD-enabled semantic pipeline supporting incremental loads, profiling, and validation. Comprehensive UAT processes and ontology workshops further drive adoption and skill transfer.
Benefits include faster onboarding, reduced rework, accelerated time‑to‑first insight, improved regulatory filing readiness, and substantial reductions in analyst wrangling and duplicate work. By establishing a scalable semantic foundation, we are increasing decision velocity and traceability across experiments, drugs, and batches while lowering the cost and time to add future use cases.
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