3 October 2019
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.
Timings: 18.30 - 19.00: Networking & Drinks
19.00 - 19.45: Drug Discovery Knowledge Graph
19.45 - 21.00: Networking