An integrated Neo4j graph database is created (Section 2.2). It contains 44,320,573 nodes and 131,916,935 relationships, which encompasses the data from multiple resources listed earlier. Our git repo (ClinicalTrials_Prediction) contains instructions on the installation, setup of the integrated data. To ensure reproducibility, the data is made public . The total count of nodes from individual databases is discussed in Section S1 (Supporting Information), the count of relations and relationship types spanning between the databases is presented in S2. Relationships across the databases, created out of complex data mapping and fusion operations, are shown with source and destination in S4. S5 contains Venn diagrams to depict the count of individual and common entities across the databases for Compound, Target, and Disease. This can offer useful insights on shared entities in the integrated data. The graph database provides visualization and data mapping, along with a flexible and extensible platform with an increased speed of execution. The scope of integration for our group, as well as other researchers, shall be extended to have a wide range of chemical databases (e.g., PubChem, KEGG, SIDER), and also non-chemical databases (e.g., USPTO). Browsing or establishing multiple federated queries across the databases is often cumbersome and computationally intensive. Hence, as a single source of information, this well-connected data can be used to discover linkage across heterogeneous resources and can serve as biological knowledge graphs for multiple use cases in bioinformatics . In addition, this can further facilitate machine learning studies on graphs (e.g., link prediction or community detection).