Learn about Drug Discovery from AstraZeneca

Michaël from AstraZeneca will talk about Biological Insights Knowledge Graph (BIKG). Join us on Wednesday, July 6, 2022, from 6:00 PM to 8:00 PM CEST!



What is BIKG?

BIKG (Biological Insights Knowledge Graph) is an internal AstraZeneca knowledge graph aimed at supporting analytics and machine learning tasks to help drug development. BIKG combines relevant data for drug development from the public as well as internal data sources to provide insights for a range of tasks: from identifying new targets to repurposing existing drugs.

BIKG integrates knowledge from heterogeneous data sources including public databases like ChEMBL or Ensembl, information extracted from full-text publications using Natural Language Processing (NLP) techniques, as well as diverse proprietary datasets collected as part of AstraZeneca drug development process and biological experimentation.

Why Knowledge Graphs?

Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the semantics underlying the used terminology. Knowledge graphs serve as training data for machine learning models and graph machine learning models in particular. Knowledge graph also provides secondary benefits at the organisation level:

  • Reduced time for data preparation
  • Improved quality: quick addition of user data on demand as well as quick feedback
  • Reduced costs: the core graph as well as common task solutions are reusableacross various use cases as well as across organisation units.

Memgraph Webinar

Sign up for the special Memgraph Webinar and learn how AstraZeneca ingests data sources in the Biological Insights Knowledge Graph (BIKG) and distributes it to data scientists and domain experts. You will also find examples of how this knowledge graph assists scientists in therapeutics development.

About the speaker

Michaël Ughetto is a graph data scientist at AstraZeneca working in the Biological Insight Knowledge Graph team. With a background in particle physics and machine learning, he is interested in graph and geometric learning. He's now focusing on applying graph technologies to drug discovery challenges.