In the drug discovery pipeline, evaluating multiple pathways between a drug target and disease is crucial for identifying potential therapeutic approaches. By examining overlapping pathways between competing drugs, researchers can uncover novel drug targets, while isoform-specific findings may elucidate unexpected clinical trial outcomes. Filtering these pathways with real-world expression and proteomics data is essential to validate new hypotheses and avoid pursuing non-viable leads.

Leveraging tools such as Neo4j, Python, and R, powered by curated databases like the QIAGEN Biomedical Knowledge Base and OmicSoft Lands, enables scientists to efficiently explore potential mechanisms of action during both target discovery and later stages of drug development.

Attendees will learn to:

• Apply pathfinding algorithms to navigate our comprehensive knowledge graph.

• Qualify drug candidates using curated scRNA-Seq expression data and detailed cell type annotations.

• Expand, filter, prioritize, and refine lists of biomarkers and drug targets through various advanced approaches.