The foundation of every machine learning predictive model in the area of biomedical informatics is a robust integrated dataset. In our experience, the big data problem is significantly pronounced in the field of chemistry (and biology). As outlined in other studies , as well as our own experience, the challenges range from the mapping of individual entities (e.g., names of drugs between ChEMBL and DrugBank), mapping entities to valid experimental data (e.g., mapping drugs from one database to activity data of the same drug in another), and so on. The data pre-processing, preparation and engineering steps are approximately 80% of the invested time in most biomedical informatics studies, and this stems from data integration being a dense process requiring strong domain expertise in addition to data wrangling proficiency. Data scientists in the area of life sciences are part computer science and part domain experts . Consequently, each research objective has to have its data integration strategy that is focused on serving the needs of that objective, and universal integration of all databases will not work in most ML-based biomedical research. In this context, we have previously synthesized the initial framework, CompoundDB4j , which contains integrated data of ChEMBL and DrugBank, to have all approved drugs in DrugBank accurately mapped to ChEMBL, such that all related annotations (e.g., bioactivities) of these compounds can be retrieved. The extended capabilities in the current instance of the framework include clinical trials and experimentally validated disease-target associations to predict clinical outcomes. The advantages of having this data in the Neo4j database are mentioned later in this section.