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Knowledge Graphs Fuel Drug Discovery: How AstraZeneca Uses Neo4j
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. Enter knowledge graphs.
Knowledge graphs combine heterogeneous data from various sources and drive intelligence into data to equip machine learning and analytics with the context they need. Knowledge graphs are the gateway to powerful graph analytics.
Join us to hear how Dr. Christos Kannas from AstraZeneca utilizes a Neo4j Reaction Knowledge Graph to integrate data from multiple sources to identify reaction data and use that as input into machine learning (ML)-driven processes to predict new reactions.
In this session, you will learn:
Knowledge graphs combine heterogeneous data from various sources and drive intelligence into data to equip machine learning and analytics with the context they need. Knowledge graphs are the gateway to powerful graph analytics.
Join us to hear how Dr. Christos Kannas from AstraZeneca utilizes a Neo4j Reaction Knowledge Graph to integrate data from multiple sources to identify reaction data and use that as input into machine learning (ML)-driven processes to predict new reactions.
In this session, you will 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