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.

Dr. Christos Kannas
Associate Principal Scientist, R&D AstraZeneca, Gothenburg, Sweden

Dr. Maya Natarajan
Senior Director, Knowledge Graphs, Neo4j

Zach Blumenfeld
Data Science Product Specialist, Neo4j

Luke Gannon
Product Manager, Neo4j

Kristof Neys
Director, Graph Data Science Technology, Neo4j

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