In the rapidly evolving landscape of drug discovery, the ability to integrate high-quality research findings into knowledge graphs is paramount. For over twenty years, our scientists have curated the relationships between genes, drugs, diseases, and pathways to power Ingenuity Pathway Analysis. Now, these data are available via our QIAGEN Biomedical KB-HD, which provides direct access to flat files, SQL APIs in Python and R, and the ability to export knowledge graph objects for analysis in Neo4j. In this talk, we will explore how to use this rich data resource to:

• aggregate relevant findings across our comprehensive disease and gene ontologies

• cross-reference clinical trial results to focus your research on genes upstream or downstream of known drug targets

• filter causal relationships by the directionality of observed effects

• combine the above methods to accelerate the drug discovery process

By demonstrating the underlying database live, we will show how the high-quality curated biomedical knowledge bases can be rapidly deployed, as well as how the underlying schema and ontologies could serve as a scaffold for integrating your own research. Overall, this demonstration will show the critical role of knowledge graphs in finding viable drug targets while avoiding potential adverse outcomes and toxicity.