Drug Discovery Knowledge Graph

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⚛️ Drug Discovery Knowledge Graph
Combinatorial chemistry has produced a huge amount of chemical libraries and data banks which include prospective drugs. However the fundamental problem still remains; how to take advantage of this data to identify the prospective nature of a compound as a vital drug? Traditional methodologies fail to provide a solution to this.

Knowledge graphs, however, provide the framework which can make drug discovery much more efficient, effective and approachable. This radical advancement in technology gives access to model biological knowledge complexity as it is found at its core. With concepts such as hyper relationships, type hierarchies, automated reasoning and analytics we can finally model, represent, and query biological knowledge at an unprecedented scale.

In this talk, we will demonstrate a methodology of using Grakn to create a drug discovery knowledge graph.

🎤 Syed Irtaza Raza, Software and Biomedical Engineer @ Grakn Labs
Syed is a Software and Biomedical Engineer at Grakn, primarily working on introducing the world on how to use a knowledge graph such as Grakn to build cognitive/intelligent systems in the Biomedical domain. To achieve this, he is implementing innovative examples as templates and ideas for how clients and community members may apply in their own specific projects of any field.

With a background in Electrical, Software and Biomedical Engineering, Syed’s mission is to discover and implement intelligent biomedical tools that are only possible with Grakn as a knowledge graph.

Timings: 18.30 - 19.00: Networking & Drinks

19.00 - 19.45: Drug Discovery Knowledge Graph

19.45 - 21.00: Networking

# Location
Auditorium
The Bradfield Centre
Cambridge Science Park
Cambridge

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