If you’re working in pharma or biotech, you likely rely on artificial intelligence (AI) to help you identify new drug targets or plausible biomarkers for disease within large data sets. Yet AI alone isn’t enough. A large proportion of Biomedical data have errors and are unstructured. For AI models to provide reliable insights, the underlying data must be of ‘high quality’, meaning it’s accurate, comprehensive, up-to-date and standardized.

Jesper Ryge (Idorsia Pharmaceuticals), Alex Jarasch (Neo4j) and Venkatesh Moktali (QIAGEN Digital Insights) come together to showcase the practical applications of high-quality biomedical relationships data from the QIAGEN Biomedical Knowledge Base (BKB) to accelerate, improve and transform research in drug discovery and pharmaceutical development. By applying AI to a gene-disease knowledge graph, they identify promising drug targets and key mechanisms underlying diseases. A brief introduction to Neo4j shows how graph-centric analysis and visualizations facilitate the effective exploration of large knowledge graphs like BKB. This integration of high-quality curated data, AI-driven analysis and advanced visualization provides valuable insights and accelerates the progress of precision medicine.

In this webinar, you’ll learn how you can:

Build disease interactomes using protein-protein interactions
Identify high-quality drug targets using inferred causal interactions
Choose targets with the least likelihood of adverse outcomes by leveraging the depth of the data in BKB
Formulate plausible hypotheses using state-of-the-art graph visualization
Don’t miss this chance to learn how to supercharge your AI toolbox to transform your drug discovery.