Whitepaper
Knowledge Graphs for Drug Discovery: What Works and What Doesn't
Learn how you can leverage knowledge graphs in drug discovery without falling into common architectural traps. Learn how to model complex biomedical evidence, execute multi-hop reasoning, and balance RDF vs. Property Graph trade-offs to accelerate scientific hypothesis testing.
What's covered
- 5 high-impact graph workflows (including multi-source data integration, multi-hop reasoning, and provenance tracking).
- Real-world biomedical applications featuring examples like Cedars-Sinai’s Alzheimer's Disease Knowledgebase (AlzKB).
- RDF vs. Property Graph analysis to help you select the right database architecture for your organization's specific workload.
- Common failure points to avoid, such as entity resolution traps, uncurated NLP extractions, and missing provenance models.
