Traditional tabular data structures fail to capture the complex, interconnected relationships that are crucial for understanding drug mechanisms and disease pathways. Pharmaceutical information scattered across multiple databases, formats, and terminologies makes it difficult to identify potential therapeutic connections using conventional approaches.

This tutorial introduces our 'MedJsonify' methodology - a Python-based framework that simplifies the process of wrangling and integrating data from diverse pharmaceutical sources. Participants will learn practical techniques for entity resolution across multiple data sources, handling medical terminology inconsistencies, and creating knowledge representations that support novel drug discovery insights.

Using prepared datasets derived from public resources like DailyMed, Purple Book, Orange Book, and ontologies from DrugBank, DO, ChEBI, and Orphanet, participants will practice key data transformation steps and visualization techniques within the Neo4j graph database environment, focusing on the most critical aspects of knowledge graph creation.

The output of this tutorial will be hands-on experience in biomedical data wrangling and a functional knowledge graph that reveals drug-disease relationships invisible in traditional tabular formats, complete with Neo4j visualization capabilities and reusable code templates.