A KG can store complex and heterogeneous data, which can be organized and interpreted using the graph topology. A format used to build a KG is the Resource Description Framework (RDF), a standard graph model for structured and semi-structured data interchange on the Web . In a KG, data can thus be stored as RDF subject-predicate-object triples, such as, for example, molecule A (subject) is found in (predicate) species X (object). These graphs can be queried using the SPARQL Protocol and RDF Query Language (SPARQL), a language designed to query and extract information from RDF databases . It is a powerful language facilitating precise and targeted retrieval of specific data elements from large and structured datasets, enabling the efficient exploration and analysis of complex information networks. Because the RDF is a W3C standard, KGs are interoperable, and it is possible to elaborate federated queries over multiple endpoints to retrieve and link data from different RDF databases (DBs). An example of a knowledge base (KB) is Wikidata, which contains more than 1 billion statements. It has a wide range of applications -from bibliographic information management to biomedical data integration -and is currently used by the life science community to disseminate FAIR (findability, accessibility, interoperability, and reusability) data and knowledge . In NPs research, Wikidata is used as a dissemination and curation platform for documented structure-organism pairs by the LOTUS initiative . KGs have also been used in drug discovery, for example to predict adverse drug reactions or help find the best candidates for drug repurposing . Thanks to their versatility, KGs can integrate standardized experimental data and public knowledge. Integrating scientific knowledge early into the data analysis workflow can enhance the discovery process by helping researchers interpret and contextualize the experimental results. Santos et al. published a brilliant example in this directionthe clinical knowledge graph -illustrating how integrating experimental clinical proteomics data and biomedical knowledge bases (KB) into a unique KG effectively enhances data analysis . In the context of drug discovery, the multilayer graph generated by the ENPKG framework allows querying of heterogeneous data to efficiently convert the large amount of data and information produced by natural extracts screening campaigns into discoveries. To confirm this, we showcased its implementation to leverage the results obtained from the phenotypic screening of 1,600 plant extracts against three trypanosomatids: Leishmania donovani (Q1950752), Trypanosoma cruzi (Q150162), and T. brucei rhodesiense (Q30216064). To illustrate the potential to interrogate heterogeneous datasets, we have further formatted and integrated a publicly available metabolomics dataset of 337 Korean medicinal plants acquired under different experimental conditions in the ENPKG . Here, we will first detail the workflow structure and its technical aspects and then show how the KG structure can help answer research questions and lead to the identification of new anti-T. cruzi and anti-L. donovani compounds. We finally discuss the current limitations of the workflow and future improvements.