Learn How Knowledge Graphs Fuel Drug Discovery at AstraZeneca
Pharmaceuticals generate hundreds of thousands of terabytes of data during all phases of R&D. However, data without relationships has very little context. Context is critical because it increases the predictive accuracy of analytics, especially machine learning (ML). This is where knowledge graphs become valuable.
Knowledge graphs combine heterogeneous data from various sources and drive intelligence into data to equip machine learning and analytics with the context they need.
Learn how Dr. Christos Kannas from AstraZeneca utilizes a Neo4j Reaction Knowledge Graph to integrate data from multiple sources to identify reaction data, and how he uses that as input into ML-driven processes to predict new reactions.
Watch this webinar to learn:
- What a knowledge graph is and how it plays a salient role in pharmaceuticals
- How knowledge graphs and graph data science analytics are essential across a pharmaceutical’s drug lifecycle pipeline
- How AstraZeneca is using a knowledge graph and graph data science analytics to boost their machine learning processes
This webinar was presented as part of the Oxford Global PharmaTec event series.
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